diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..7c671284d4d7b61ecc3636e1b8e7e96c8427061d 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/step_10361 filter=lfs diff=lfs merge=lfs -text
+Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/step_20722 filter=lfs diff=lfs merge=lfs -text
+Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/step_31083 filter=lfs diff=lfs merge=lfs -text
+Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/step_41444 filter=lfs diff=lfs merge=lfs -text
diff --git a/Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/all_config.yaml b/Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/all_config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d92e2e130fe9ca041072a7ad8bb339713e1b302c
--- /dev/null
+++ b/Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/all_config.yaml
@@ -0,0 +1,61 @@
+arch:
+  H_cycles: 3
+  H_layers: 2
+  L_cycles: 1
+  L_layers: 6
+  dep_rank: 64
+  dep_topk: 12
+  expansion: 4.0
+  forward_dtype: bfloat16
+  glps_dep_graph: true
+  glps_enabled: true
+  glps_fill_obvious: true
+  glps_global_propagate_on_low_conf: true
+  glps_max_targeted_iters: 4
+  glps_tau_halt: 0.95
+  glps_tau_uncertain: 0.8
+  glps_token_masking: true
+  halt_exploration_prob: 0.1
+  halt_max_steps: 16
+  hidden_size: 512
+  loss:
+    loss_type: stablemax_cross_entropy
+    name: losses@ACTLossHead
+  mlp_t: false
+  name: recursive_reasoning.glps@GLPS_ACTV1
+  num_heads: 8
+  pos_encodings: rope
+  puzzle_emb_ndim: 512
+  rms_norm_eps: 1.0e-05
+  rope_theta: 10000.0
+beta1: 0.9
+beta2: 0.95
+checkpoint_every_eval: true
+checkpoint_path: checkpoints/Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1
+data_paths:
+- data/arc1concept-aug-1000
+data_paths_test: []
+ema: true
+ema_rate: 0.999
+epochs: 100000
+eval_glps_max_targeted_iters: null
+eval_glps_tau_halt: null
+eval_halt_max_steps: null
+eval_interval: 2000
+eval_only: false
+eval_save_outputs: []
+evaluators:
+- name: arc@ARC
+freeze_weights: false
+global_batch_size: 768
+load_checkpoint: null
+lr: 0.0001
+lr_min_ratio: 0.3
+lr_warmup_steps: 2000
+min_eval_interval: 0
+project_name: Arc1concept-aug-1000-ACT-torch
+puzzle_emb_lr: 0.01
+puzzle_emb_weight_decay: 0.1
+run_name: pretrain_glps_arc1_h200_v1
+seed: 0
+weight_decay: 0.1
diff --git a/Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/evaluator_ARC_step_10361/submission.json b/Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/evaluator_ARC_step_10361/submission.json
new file mode 100644
index 0000000000000000000000000000000000000000..9d25003cd516148c509627b62f4baa503f7ac4ac
--- /dev/null
+++ b/Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/evaluator_ARC_step_10361/submission.json
@@ -0,0 +1 @@
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diff --git a/Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/evaluator_ARC_step_20722/submission.json b/Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/evaluator_ARC_step_20722/submission.json
new file mode 100644
index 0000000000000000000000000000000000000000..f1ede4e0520cab674f116ff04443ff054a4dbf05
--- /dev/null
+++ b/Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/evaluator_ARC_step_20722/submission.json
@@ -0,0 +1 @@
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diff --git a/Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/evaluator_ARC_step_31083/submission.json b/Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/evaluator_ARC_step_31083/submission.json
new file mode 100644
index 0000000000000000000000000000000000000000..59d3793c4044db63557b7992dcc1428ca24c25c8
--- /dev/null
+++ b/Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/evaluator_ARC_step_31083/submission.json
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diff --git a/Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/evaluator_ARC_step_41444/submission.json b/Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/evaluator_ARC_step_41444/submission.json
new file mode 100644
index 0000000000000000000000000000000000000000..01b151de497eb1759b43646660eee6ee76261f45
--- /dev/null
+++ b/Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/evaluator_ARC_step_41444/submission.json
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0, 0], [0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 8, 2, 2, 0, 2, 2, 8, 2, 2, 0, 2, 2, 8, 2, 2, 0, 0, 0, 0], [0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 0, 0, 0], [0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 0, 0, 0], [0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0], [0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 8, 2, 2, 0, 2, 2, 8, 2, 2, 0, 2, 2, 8, 2, 2, 0, 0, 0, 0], [0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 0, 0, 0], [0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 0, 0, 0], [0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0], [0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attempt_2": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 8, 2, 2, 0, 2, 2, 8, 2, 2, 0, 2, 2, 8, 2, 2, 0, 0, 0, 0], [0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 0, 0, 0], [0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 0, 0, 0], [0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0], [0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 8, 2, 2, 0, 2, 2, 2, 2, 2, 0, 2, 2, 8, 2, 2, 0, 0, 0, 0], [0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 0, 0, 0], [0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 0, 0, 0], [0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0], [0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 8, 2, 2, 0, 2, 2, 8, 2, 2, 0, 2, 2, 8, 2, 2, 0, 0, 0, 0], [0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 0, 0, 0], [0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 0, 0, 0], [0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0], [0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], "72a961c9": [{"attempt_1": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 0, 8, 0, 0, 0, 8, 0, 2, 0], [0, 2, 0, 8, 0, 0, 0, 8, 0, 2, 0], [0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0], [0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0], [1, 2, 1, 8, 1, 1, 1, 8, 1, 2, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attempt_2": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 2, 1, 8, 1, 1, 1, 8, 1, 2, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], "7039b2d7": [{"attempt_1": [[1, 1, 1], [1, 1, 1], [1, 1, 1]], "attempt_2": [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]}], "281123b4": [{"attempt_1": [[9, 8, 8, 5], [4, 4, 9, 9], [8, 4, 5, 9], [9, 9, 9, 8]], "attempt_2": [[9, 8, 8, 5], [4, 4, 9, 9], [8, 4, 5, 9], [9, 9, 9, 5]]}], "b457fec5": [{"attempt_1": [[0, 0, 0], [0, 0, 0], [0, 0, 0]], "attempt_2": [[0, 0, 0], [0, 2, 0], [0, 0, 0]]}], "62ab2642": [{"attempt_1": [[0, 5, 8, 8, 8, 8, 8, 5, 0, 0, 5, 0], [0, 5, 8, 8, 8, 5, 5, 5, 0, 5, 5, 0], [5, 5, 5, 8, 8, 5, 0, 5, 5, 5, 0, 0], [0, 0, 5, 5, 5, 5, 0, 5, 0, 5, 5, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 5, 0]], "attempt_2": [[0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0], [0, 5, 0, 0, 0, 5, 5, 5, 0, 5, 5, 0], [5, 5, 5, 0, 0, 5, 0, 5, 5, 5, 0, 0], [0, 0, 5, 5, 5, 5, 0, 5, 0, 5, 5, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 5, 0]]}]}
\ No newline at end of file
diff --git a/Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/glps.py b/Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/glps.py
new file mode 100644
index 0000000000000000000000000000000000000000..96e6de932e79e174cf773779293a643842e8a268
--- /dev/null
+++ b/Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/glps.py
@@ -0,0 +1,409 @@
+
+from typing import Tuple, List, Dict
+from dataclasses import dataclass
+import math
+import torch
+import torch.nn.functional as F
+from torch import nn
+from pydantic import BaseModel
+
+# Reuse the same building blocks as HRM/TRM
+from models.common import trunc_normal_init_
+from models.layers import rms_norm, SwiGLU, Attention, RotaryEmbedding, CosSin, CastedEmbedding, CastedLinear
+from models.sparse_embedding import CastedSparseEmbedding
+
+"""
+Global-Local Predictive Solver (GLPS)
+------------------------------------
+A light-weight control-policy on top of the HRM/TRM style blocks:
+- H1: global scan -> certainty map
+- L1: fill-obvious (lock stable cells)
+- H2: dependency scoring over remaining cells
+- L2: targeted refinement (masked updates)
+- H3: energy-based confidence -> (optional) one global propagate sweep -> halt
+
+This file keeps parameter growth tiny: a few heads + (optional) low-rank dependency scorer.
+"""
+
+@dataclass
+class GLPS_ACTV1InnerCarry:
+    z_H: torch.Tensor
+    z_L: torch.Tensor
+
+@dataclass
+class GLPS_ACTV1Carry:
+    inner_carry: GLPS_ACTV1InnerCarry
+    steps: torch.Tensor
+    halted: torch.Tensor
+    current_data: Dict[str, torch.Tensor]
+
+class GLPS_ACTV1Config(BaseModel):
+    # Core IO / shapes
+    batch_size: int
+    seq_len: int
+    puzzle_emb_ndim: int = 0
+    num_puzzle_identifiers: int = 1
+    vocab_size: int = 256
+
+    # Cycle schedule
+    H_cycles: int = 3              # (scan -> refine -> check) typical
+    L_cycles: int = 1
+
+    # Depth
+    H_layers: int = 2
+    L_layers: int = 4
+
+    # Transformer config
+    hidden_size: int = 512
+    expansion: float = 2.0
+    num_heads: int = 8
+    pos_encodings: str = "rope"
+
+    rms_norm_eps: float = 1e-5
+    rope_theta: float = 10000.0
+
+    # ACT wrapper
+    halt_max_steps: int = 4
+    halt_exploration_prob: float = 0.1
+
+    forward_dtype: str = "bfloat16"
+
+    # Optional: use MLP on L instead of attention (matches HRM/TRM option)
+    mlp_t: bool = False
+
+    # ---- GLPS extras (tiny) ----
+    glps_enabled: bool = True
+    glps_fill_obvious: bool = True
+    glps_dep_graph: bool = True
+    glps_token_masking: bool = True
+    glps_global_propagate_on_low_conf: bool = True
+
+    glps_tau_halt: float = 0.95       # final confidence to halt
+    glps_tau_uncertain: float = 0.60  # cell-wise certainty threshold
+    glps_max_targeted_iters: int = 2  # small number: 1-2
+
+    # Dependency scorer (low rank bilinear)
+    dep_rank: int = 32
+    dep_topk: int = 8
+
+class GLPSBlock(nn.Module):
+    def __init__(self, config: GLPS_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        if self.config.mlp_t:
+            self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size)
+            self.mlp_t = SwiGLU(
+                hidden_size=self.config.seq_len + self.puzzle_emb_len, # treat sequence as channel
+                expansion=config.expansion,
+            )
+        else:
+            self.self_attn = Attention(
+                hidden_size=config.hidden_size,
+                head_dim=config.hidden_size // config.num_heads,
+                num_heads=config.num_heads,
+                num_key_value_heads=config.num_heads,
+                causal=False,
+            )
+        self.mlp = SwiGLU(hidden_size=config.hidden_size, expansion=config.expansion)
+        self.norm_eps = config.rms_norm_eps
+
+    def forward(self, cos_sin: CosSin, hidden_states: torch.Tensor) -> torch.Tensor:
+        if self.config.mlp_t:
+            # MLP over sequence dimension (mlp-t)
+            hidden_states = hidden_states.transpose(1, 2)
+            out = self.mlp_t(hidden_states)
+            hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+            hidden_states = hidden_states.transpose(1, 2)
+        else:
+            hidden_states = rms_norm(
+                hidden_states + self.self_attn(cos_sin=cos_sin, hidden_states=hidden_states),
+                variance_epsilon=self.norm_eps,
+            )
+        out = self.mlp(hidden_states)
+        hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+        return hidden_states
+
+class GLPSReasoningModule(nn.Module):
+    """Reasoning stack with optional masked updates (only update uncertain tokens)."""
+    def __init__(self, layers: List[GLPSBlock]):
+        super().__init__()
+        self.layers = torch.nn.ModuleList(layers)
+
+    def forward(self, hidden_states: torch.Tensor, input_injection: torch.Tensor, update_mask: torch.Tensor = None, **kwargs) -> torch.Tensor:
+        x = hidden_states
+        for layer in self.layers:
+            # Compute candidate update using injected context
+            y = layer(hidden_states=x + input_injection, **kwargs)
+            if update_mask is not None:
+                # Convex blend keeps frozen tokens unchanged
+                m = update_mask.to(x.dtype)[..., None]
+                x = x + m * (y - x)
+            else:
+                x = y
+        return x
+
+class GLPS_ACTV1_Inner(nn.Module):
+    def __init__(self, config: GLPS_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        self.forward_dtype = getattr(torch, self.config.forward_dtype)
+
+        # I/O
+        self.embed_scale  = math.sqrt(self.config.hidden_size)
+        embed_init_std = 1.0 / self.embed_scale
+
+        self.embed_tokens = CastedEmbedding(self.config.vocab_size, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+        self.lm_head      = CastedLinear(self.config.hidden_size, self.config.vocab_size, bias=False)
+        self.q_head       = CastedLinear(self.config.hidden_size, 2, bias=True)
+
+        # Puzzle emb (optional) — same convention as HRM/TRM
+        self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size)  # ceil div
+        if self.config.puzzle_emb_ndim > 0:
+            self.puzzle_emb = CastedSparseEmbedding(self.config.num_puzzle_identifiers, self.config.puzzle_emb_ndim, batch_size=self.config.batch_size, init_std=0, cast_to=self.forward_dtype)
+
+        # Positional encodings
+        if self.config.pos_encodings == "rope":
+            self.rotary_emb = RotaryEmbedding(dim=self.config.hidden_size // self.config.num_heads, max_position_embeddings=self.config.seq_len + self.puzzle_emb_len, base=self.config.rope_theta)
+        elif self.config.pos_encodings == "learned":
+            self.embed_pos = CastedEmbedding(self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+
+        # Reasoning stacks
+        self.H_level = GLPSReasoningModule(layers=[GLPSBlock(self.config) for _ in range(self.config.H_layers)])
+        self.L_level = GLPSReasoningModule(layers=[GLPSBlock(self.config) for _ in range(self.config.L_layers)])
+
+        # Initial states (match HRM/TRM style)
+        H_init = trunc_normal_init_(torch.empty(1, 1, self.config.hidden_size, dtype=self.forward_dtype), std=1.0)
+        L_init = trunc_normal_init_(torch.empty(1, 1, self.config.hidden_size, dtype=self.forward_dtype), std=1.0)
+        self.register_buffer("H_init", H_init, persistent=True)
+        self.register_buffer("L_init", L_init, persistent=True)
+
+        # GLPS small heads
+        self.candidate_head = CastedLinear(self.config.hidden_size, 16, bias=True)  # task-specific; for Sudoku you can slice to 9
+        self.certainty_head = CastedLinear(self.config.hidden_size, 1, bias=True)
+        self.energy_head    = CastedLinear(self.config.hidden_size, 1, bias=True)
+
+        # Low-rank dependency scorer (shared)
+        r = max(1, self.config.dep_rank)
+        self.dep_q = CastedLinear(self.config.hidden_size, r, bias=False)
+        self.dep_k = CastedLinear(self.config.hidden_size, r, bias=False)
+
+        # Q head init like HRM/TRM (near-zero -> easier bootstrapping)
+        with torch.no_grad():
+            self.q_head.weight.zero_()
+            self.q_head.bias.fill_(-5)
+
+    def _input_embeddings(self, input: torch.Tensor, puzzle_identifiers: torch.Tensor):
+        # Token embedding
+        embedding = self.embed_tokens(input.to(torch.int32))
+
+        # Puzzle embeddings
+        if self.config.puzzle_emb_ndim > 0:
+            puzzle_embedding = self.puzzle_emb(puzzle_identifiers)
+            pad_count = self.puzzle_emb_len * self.config.hidden_size - puzzle_embedding.shape[-1]
+            if pad_count > 0:
+                puzzle_embedding = F.pad(puzzle_embedding, (0, pad_count))
+            embedding = torch.cat((puzzle_embedding.view(-1, self.puzzle_emb_len, self.config.hidden_size), embedding), dim=-2)
+
+        # Position embeddings
+        if self.config.pos_encodings == "learned":
+            embedding = 0.707106781 * (embedding + self.embed_pos.embedding_weight.to(self.forward_dtype))
+
+        return self.embed_scale * embedding
+
+    def empty_carry(self, batch_size: int):
+        return GLPS_ACTV1InnerCarry(
+            z_H=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+            z_L=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+        )
+
+    def reset_carry(self, reset_flag: torch.Tensor, carry: GLPS_ACTV1InnerCarry):
+        # Explicitly expand buffers and mask to target shapes to avoid shape confusion
+        B, L, D = carry.z_H.shape
+        # Reduce/reset flag to per-batch boolean vector of shape [B]
+        if reset_flag.ndim == 1 and reset_flag.shape[0] == B:
+            reset_b = reset_flag.to(torch.bool)
+        else:
+            # If shape is [B, ...] reduce across non-batch dims; otherwise fallback to first B entries
+            try:
+                reset_b = reset_flag.reshape(B, -1).any(dim=1).to(torch.bool)
+            except Exception:
+                reset_b = reset_flag.reshape(-1)[:B].to(torch.bool)
+        m = reset_b.view(B, 1, 1)
+        mH = m.expand(B, L, D)
+        mL = mH  # same shape for z_L
+        H_init_exp = self.H_init.expand(B, L, D)
+        L_init_exp = self.L_init.expand(B, L, D)
+        return GLPS_ACTV1InnerCarry(
+            z_H=torch.where(mH, H_init_exp, carry.z_H),
+            z_L=torch.where(mL, L_init_exp, carry.z_L),
+        )
+
+    def _global_scan(self, z_L, z_H, input_embeddings, seq_info):
+        # One light pass to gather global signals
+        z_scan = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+        cand_logits = self.candidate_head(z_scan)              # [B, L, C]
+        certainty   = torch.sigmoid(self.certainty_head(z_scan))  # [B, L, 1]
+        return z_scan, cand_logits, certainty
+
+    def _build_dep_mask(self, z_ctx, uncertain_mask: torch.Tensor):
+        """Compute a dependency-based focus mask from a low-rank bilinear score.
+        uncertain_mask: [B, L] boolean mask of cells that are currently uncertain
+        Returns: dep_mask [B, L] boolean mask of cells to (re)update.
+        """
+        B, L, D = z_ctx.shape
+        # Project to low-rank space; use float32 to avoid dtype mismatches under torch.compile
+        Q = self.dep_q(z_ctx).to(torch.float32)   # [B, L, r]
+        K = self.dep_k(z_ctx).to(torch.float32)   # [B, L, r]
+        r = max(1, int(Q.shape[-1]))
+        sim = torch.matmul(Q, K.transpose(1, 2))  # [B, L, L] (float32)
+        sim = sim / math.sqrt(r)
+
+        # Aggregate influence from uncertain queries onto target tokens
+        src = uncertain_mask.to(sim.dtype).unsqueeze(1)      # [B, 1, L]
+        influence = torch.matmul(src, sim).squeeze(1)        # [B, L]
+
+        # Top-k influenced tokens per batch
+        topk = min(self.config.dep_topk, L)
+        vals, idx = torch.topk(influence, k=topk, dim=-1)    # [B, topk]
+        dep_mask = torch.zeros_like(uncertain_mask)
+        dep_mask.scatter_(1, idx, True)
+
+        # Always include uncertain cells themselves
+        dep_mask = dep_mask | uncertain_mask
+        return dep_mask
+
+    def forward(self, carry: GLPS_ACTV1InnerCarry, batch: Dict[str, torch.Tensor]):
+        seq_info = dict(cos_sin=getattr(self, "rotary_emb", None)() if hasattr(self, "rotary_emb") else None)
+
+        # Encode inputs
+        input_embeddings = self._input_embeddings(batch["inputs"], batch["puzzle_identifiers"])
+
+        # States
+        z_H, z_L = carry.z_H, carry.z_L
+
+        if not self.config.glps_enabled:
+            # Fallback to an HRM-like single-cycle grad update for compatibility
+            with torch.no_grad():
+                for _H in range(self.config.H_cycles - 1):
+                    for _L in range(self.config.L_cycles):
+                        z_L = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+                    z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+            # final grad step
+            for _L in range(self.config.L_cycles):
+                z_L = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+            z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+
+            # Outputs
+            new_carry = GLPS_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach())
+            logits = self.lm_head(z_H)[:, self.puzzle_emb_len:]
+            q_logits = self.q_head(z_H[:, 0]).to(torch.float32)
+            conf = torch.zeros_like(q_logits[..., :1]) + 0.5  # neutral
+            return new_carry, logits, (q_logits[..., 0], q_logits[..., 1]), conf
+
+        # ===== GLPS path =====
+        # H1: global scan (cheap)
+        with torch.no_grad():
+            z_scan, cand_logits, certainty = self._global_scan(z_L, z_H, input_embeddings, seq_info)
+
+        # L1: fill-obvious -> compute stable vs uncertain masks
+        if self.config.glps_fill_obvious:
+            obvious_mask = (certainty >= self.config.glps_tau_uncertain)  # [B, L, 1]
+        else:
+            obvious_mask = torch.zeros_like(certainty).bool()
+        stable_mask = obvious_mask.squeeze(-1)         # [B, L]
+        uncertain_mask = ~stable_mask                  # [B, L]
+
+        # H2: dependency prediction over remaining cells
+        if self.config.glps_dep_graph:
+            dep_mask = self._build_dep_mask(z_scan, uncertain_mask)   # [B, L]
+        else:
+            dep_mask = uncertain_mask
+
+        # L2: targeted refinement (a couple of masked iters)
+        update_mask = dep_mask if self.config.glps_token_masking else None
+        z = z_scan.detach()  # use scanned context as start
+        for _ in range(self.config.glps_max_targeted_iters):
+            z = self.L_level(z, z_H + input_embeddings, update_mask=update_mask, **seq_info)
+            # Refresh certainty to shrink mask (optional but cheap)
+            cert_now = torch.sigmoid(self.certainty_head(z)).squeeze(-1)
+            if self.config.glps_token_masking:
+                update_mask = dep_mask & (cert_now < self.config.glps_tau_uncertain)
+
+        # Merge into H and do a light H update with grad
+        z_L = z
+        z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+
+        # H3: energy/consistency -> confidence & optional global propagate
+        energy = torch.sigmoid(self.energy_head(z_H)).mean(dim=1)  # [B, 1]
+        conf = 1.0 - energy
+        need_sweep = (conf.squeeze(-1) < self.config.glps_tau_halt)
+        if self.config.glps_global_propagate_on_low_conf and need_sweep.any():
+            # one final full sweep only for rows needing it
+            maskB = need_sweep.view(-1, 1, 1)
+            zL2 = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+            zH2 = self.H_level(z_H, zL2, update_mask=None, **seq_info)
+            z_L = torch.where(maskB, zL2, z_L)
+            z_H = torch.where(maskB, zH2, z_H)
+
+        # Outputs
+        new_carry = GLPS_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach())
+        logits = self.lm_head(z_H)[:, self.puzzle_emb_len:]
+        q_logits = self.q_head(z_H[:, 0]).to(torch.float32)
+        return new_carry, logits, (q_logits[..., 0], q_logits[..., 1]), conf
+
+class GLPS_ACTV1(nn.Module):
+    """ACT-style wrapper that mixes Q-halt with GLPS confidence."""
+    def __init__(self, config_dict: dict):
+        super().__init__()
+        self.config = GLPS_ACTV1Config(**config_dict)
+        self.inner = GLPS_ACTV1_Inner(self.config)
+
+    @property
+    def puzzle_emb(self):
+        return self.inner.puzzle_emb
+
+    def initial_carry(self, batch: Dict[str, torch.Tensor]):
+        batch_size = batch["inputs"].shape[0]
+        return GLPS_ACTV1Carry(
+            inner_carry=self.inner.empty_carry(batch_size),
+            steps=torch.zeros((batch_size,), dtype=torch.int32),
+            halted=torch.ones((batch_size,), dtype=torch.bool),  # start halted to force reset on first pass
+            current_data={k: torch.empty_like(v) for k, v in batch.items()}
+        )
+
+    def forward(self, carry: GLPS_ACTV1Carry, batch: Dict[str, torch.Tensor]):
+        # Reset halted seqs
+        new_inner_carry = self.inner.reset_carry(carry.halted, carry.inner_carry)
+        new_steps = torch.where(carry.halted, 0, carry.steps)
+        new_current_data = {k: torch.where(carry.halted.view((-1,) + (1,) * (batch[k].ndim - 1)), batch[k], v) for k, v in carry.current_data.items()}
+
+        # Inner step
+        new_inner_carry, logits, (q_halt_logits, q_continue_logits), conf = self.inner(new_inner_carry, new_current_data)
+
+        outputs = {
+            "logits": logits,
+            "q_halt_logits": q_halt_logits,
+            "q_continue_logits": q_continue_logits,
+            "conf": conf.squeeze(-1),
+        }
+
+        with torch.no_grad():
+            new_steps = new_steps + 1
+            is_last_step = new_steps >= self.config.halt_max_steps
+
+            # Combine halt signals: Q or confidence or last-step
+            halted = is_last_step | (q_halt_logits > q_continue_logits) | (conf.squeeze(-1) >= self.config.glps_tau_halt)
+
+            # Exploration during training only
+            if self.training and (self.config.halt_max_steps > 1):
+                min_halt_steps = (torch.rand_like(q_halt_logits) < self.config.halt_exploration_prob) * torch.randint_like(new_steps, low=2, high=self.config.halt_max_steps + 1)
+                halted = halted & (new_steps >= min_halt_steps)
+                
+                # Optional target for Q-learning (kept similar to HRM)
+                next_conf = self.inner(new_inner_carry, new_current_data)[-1]
+                outputs["target_q_continue"] = torch.sigmoid(torch.where(is_last_step, q_halt_logits, torch.maximum(q_halt_logits, q_continue_logits)))
+            else:
+                # During eval, always use max_steps to ensure consistent reasoning depth (same as TRM/HRM eval behavior)
+                halted = is_last_step
+
+        return GLPS_ACTV1Carry(new_inner_carry, new_steps, halted, new_current_data), outputs
diff --git a/Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/losses.py b/Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..cdfb77718bc16f98cdd7da06eb5795c36b866aa7
--- /dev/null
+++ b/Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/losses.py
@@ -0,0 +1,102 @@
+from typing import Any, Tuple, Dict, Sequence, Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+import math
+
+IGNORE_LABEL_ID = -100
+
+
+def s(x, epsilon=1e-30):
+    return torch.where(
+        x<0,
+        1/(1-x+ epsilon),
+        x + 1
+    )
+
+
+def log_stablemax(x, dim=-1):
+    s_x = s(x)
+    return torch.log(s_x/torch.sum(s_x, dim=dim, keepdim=True))
+
+
+def stablemax_cross_entropy(logits, labels, ignore_index: int = -100, valid_mask=None):
+    logprobs = log_stablemax(logits.to(torch.float64), dim=-1)
+
+    if valid_mask is None:
+        valid_mask = (labels != ignore_index)
+    transformed_labels = torch.where(valid_mask, labels, 0)
+    prediction_logprobs = torch.gather(logprobs, index=transformed_labels.to(torch.long).unsqueeze(-1), dim=-1).squeeze(-1)
+
+    return -torch.where(valid_mask, prediction_logprobs, 0)
+
+
+def softmax_cross_entropy(logits, labels, ignore_index: int = -100):
+    # Cast logits to f32
+    # Flatten logits
+    return F.cross_entropy(logits.to(torch.float32).view(-1, logits.shape[-1]), labels.to(torch.long).view(-1), ignore_index=ignore_index, reduction="none").view(labels.shape)
+
+
+class ACTLossHead(nn.Module):
+    def __init__(self, model: nn.Module, loss_type: str):
+        super().__init__()
+        self.model = model
+        self.loss_fn = globals()[loss_type]
+        
+    def initial_carry(self, *args, **kwargs):
+        return self.model.initial_carry(*args, **kwargs)  # type: ignore
+
+    def forward(
+        self,
+        return_keys: Sequence[str],
+        # Model args
+        **model_kwargs,
+    ) -> Tuple[Any, torch.Tensor, Dict[str, torch.Tensor], Optional[Dict[str, torch.Tensor]], torch.Tensor]:
+        # Model logits
+        # B x SeqLen x D
+        new_carry, outputs = self.model(**model_kwargs)
+        labels = new_carry.current_data["labels"]
+
+        with torch.no_grad():
+            # Preds
+            outputs["preds"] = torch.argmax(outputs["logits"], dim=-1)
+
+            # Correctness
+            mask = (labels != IGNORE_LABEL_ID)
+            loss_counts = mask.sum(-1)
+            loss_divisor = loss_counts.clamp_min(1).unsqueeze(-1)  # Avoid NaNs in division
+
+            is_correct = mask & (torch.argmax(outputs["logits"], dim=-1) == labels)
+            seq_is_correct = is_correct.sum(-1) == loss_counts
+            
+            # Metrics (halted)
+            valid_metrics = new_carry.halted & (loss_counts > 0)
+            metrics = {
+                "count": valid_metrics.sum(),
+                
+                "accuracy":       torch.where(valid_metrics, (is_correct.to(torch.float32) / loss_divisor).sum(-1), 0).sum(),
+                "exact_accuracy": (valid_metrics & seq_is_correct).sum(),
+
+                "q_halt_accuracy": (valid_metrics & ((outputs["q_halt_logits"] >= 0) == seq_is_correct)).sum(),
+                "steps":          torch.where(valid_metrics, new_carry.steps, 0).sum(),
+            }
+
+        # Losses
+
+        lm_loss = (self.loss_fn(outputs["logits"], labels, ignore_index=IGNORE_LABEL_ID, valid_mask=mask) / loss_divisor).sum()
+        q_halt_loss = F.binary_cross_entropy_with_logits(outputs["q_halt_logits"], seq_is_correct.to(outputs["q_halt_logits"].dtype), reduction="sum")
+        metrics.update({
+            "lm_loss": lm_loss.detach(),
+            "q_halt_loss": q_halt_loss.detach(),
+        })
+        # Q continue (bootstrapping target loss); Alexia: This fits Q-learning, but seems totally unecessary
+        q_continue_loss = 0
+        if "target_q_continue" in outputs:
+            q_continue_loss = F.binary_cross_entropy_with_logits(outputs["q_continue_logits"], outputs["target_q_continue"], reduction="sum")
+
+            metrics["q_continue_loss"] = q_continue_loss.detach()
+        # Filter outputs for return
+        detached_outputs = {k: outputs[k].detach() for k in return_keys if k in outputs}
+
+        return new_carry, lm_loss + 0.5 * (q_halt_loss + q_continue_loss), metrics, detached_outputs, new_carry.halted.all()
diff --git a/Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/step_10361 b/Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/step_10361
new file mode 100644
index 0000000000000000000000000000000000000000..50aae64012924a4bca6c14a197ab9877075cfca9
--- /dev/null
+++ b/Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/step_10361
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:16fe5d9f72d90890d9de1ebcec61294dff2f7ca98f75442d000e338993a9945f
+size 1904307893
diff --git a/Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/step_20722 b/Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/step_20722
new file mode 100644
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--- /dev/null
+++ b/Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/step_20722
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:36344623a0bb8305681ad97ff31e5b8dc0af6f15ff83fd0ffcaa8c770d1601f1
+size 1904307893
diff --git a/Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/step_31083 b/Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/step_31083
new file mode 100644
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--- /dev/null
+++ b/Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/step_31083
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c39d0dc62d8c9ecfc11c9f7cf48aedeec307e731b0c785c9e4e6c262496786c2
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diff --git a/Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/step_41444 b/Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/step_41444
new file mode 100644
index 0000000000000000000000000000000000000000..8f57f7ebc399389316fe01636fb69322313da813
--- /dev/null
+++ b/Arc1concept-aug-1000-ACT-torch/pretrain_glps_arc1_h200_v1/step_41444
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6259ae684f464a5605db790399e6fac7d7228b63ea1273c48616c5e03ab92843
+size 1904307893
diff --git a/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_cos/all_config.yaml b/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_cos/all_config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..36745eed9d4976a171bfbae6a4736c1155e49c3b
--- /dev/null
+++ b/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_cos/all_config.yaml
@@ -0,0 +1,60 @@
+arch:
+  H_cycles: 3
+  H_layers: 2
+  L_cycles: 1
+  L_layers: 7
+  dep_rank: 64
+  dep_topk: 12
+  expansion: 4.0
+  forward_dtype: bfloat16
+  glps_dep_graph: true
+  glps_enabled: true
+  glps_fill_obvious: true
+  glps_global_propagate_on_low_conf: true
+  glps_max_targeted_iters: 4
+  glps_tau_halt: 0.95
+  glps_tau_uncertain: 0.8
+  glps_token_masking: true
+  halt_exploration_prob: 0.1
+  halt_max_steps: 16
+  hidden_size: 512
+  loss:
+    loss_type: stablemax_cross_entropy
+    name: losses@ACTLossHead
+  mlp_t: false
+  name: recursive_reasoning.glps@GLPS_ACTV1
+  num_heads: 8
+  pos_encodings: rope
+  puzzle_emb_ndim: 0
+  rms_norm_eps: 1.0e-05
+  rope_theta: 10000.0
+beta1: 0.9
+beta2: 0.95
+checkpoint_every_eval: true
+checkpoint_path: checkpoints/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_cos
+data_paths:
+- data/maze-30x30-hard-1k
+data_paths_test: []
+ema: true
+ema_rate: 0.999
+epochs: 50000
+eval_glps_max_targeted_iters: null
+eval_glps_tau_halt: null
+eval_halt_max_steps: null
+eval_interval: 5000
+eval_only: false
+eval_save_outputs: []
+evaluators: []
+freeze_weights: false
+global_batch_size: 64
+load_checkpoint: null
+lr: 0.0001
+lr_min_ratio: 0.1
+lr_warmup_steps: 2000
+min_eval_interval: 0
+project_name: Maze-30x30-hard-1k-ACT-torch
+puzzle_emb_lr: 0.0001
+puzzle_emb_weight_decay: 1.0
+run_name: pretrain_glps_maze30_v1_cos
+seed: 0
+weight_decay: 1.0
diff --git a/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_cos/glps.py b/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_cos/glps.py
new file mode 100644
index 0000000000000000000000000000000000000000..96e6de932e79e174cf773779293a643842e8a268
--- /dev/null
+++ b/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_cos/glps.py
@@ -0,0 +1,409 @@
+
+from typing import Tuple, List, Dict
+from dataclasses import dataclass
+import math
+import torch
+import torch.nn.functional as F
+from torch import nn
+from pydantic import BaseModel
+
+# Reuse the same building blocks as HRM/TRM
+from models.common import trunc_normal_init_
+from models.layers import rms_norm, SwiGLU, Attention, RotaryEmbedding, CosSin, CastedEmbedding, CastedLinear
+from models.sparse_embedding import CastedSparseEmbedding
+
+"""
+Global-Local Predictive Solver (GLPS)
+------------------------------------
+A light-weight control-policy on top of the HRM/TRM style blocks:
+- H1: global scan -> certainty map
+- L1: fill-obvious (lock stable cells)
+- H2: dependency scoring over remaining cells
+- L2: targeted refinement (masked updates)
+- H3: energy-based confidence -> (optional) one global propagate sweep -> halt
+
+This file keeps parameter growth tiny: a few heads + (optional) low-rank dependency scorer.
+"""
+
+@dataclass
+class GLPS_ACTV1InnerCarry:
+    z_H: torch.Tensor
+    z_L: torch.Tensor
+
+@dataclass
+class GLPS_ACTV1Carry:
+    inner_carry: GLPS_ACTV1InnerCarry
+    steps: torch.Tensor
+    halted: torch.Tensor
+    current_data: Dict[str, torch.Tensor]
+
+class GLPS_ACTV1Config(BaseModel):
+    # Core IO / shapes
+    batch_size: int
+    seq_len: int
+    puzzle_emb_ndim: int = 0
+    num_puzzle_identifiers: int = 1
+    vocab_size: int = 256
+
+    # Cycle schedule
+    H_cycles: int = 3              # (scan -> refine -> check) typical
+    L_cycles: int = 1
+
+    # Depth
+    H_layers: int = 2
+    L_layers: int = 4
+
+    # Transformer config
+    hidden_size: int = 512
+    expansion: float = 2.0
+    num_heads: int = 8
+    pos_encodings: str = "rope"
+
+    rms_norm_eps: float = 1e-5
+    rope_theta: float = 10000.0
+
+    # ACT wrapper
+    halt_max_steps: int = 4
+    halt_exploration_prob: float = 0.1
+
+    forward_dtype: str = "bfloat16"
+
+    # Optional: use MLP on L instead of attention (matches HRM/TRM option)
+    mlp_t: bool = False
+
+    # ---- GLPS extras (tiny) ----
+    glps_enabled: bool = True
+    glps_fill_obvious: bool = True
+    glps_dep_graph: bool = True
+    glps_token_masking: bool = True
+    glps_global_propagate_on_low_conf: bool = True
+
+    glps_tau_halt: float = 0.95       # final confidence to halt
+    glps_tau_uncertain: float = 0.60  # cell-wise certainty threshold
+    glps_max_targeted_iters: int = 2  # small number: 1-2
+
+    # Dependency scorer (low rank bilinear)
+    dep_rank: int = 32
+    dep_topk: int = 8
+
+class GLPSBlock(nn.Module):
+    def __init__(self, config: GLPS_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        if self.config.mlp_t:
+            self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size)
+            self.mlp_t = SwiGLU(
+                hidden_size=self.config.seq_len + self.puzzle_emb_len, # treat sequence as channel
+                expansion=config.expansion,
+            )
+        else:
+            self.self_attn = Attention(
+                hidden_size=config.hidden_size,
+                head_dim=config.hidden_size // config.num_heads,
+                num_heads=config.num_heads,
+                num_key_value_heads=config.num_heads,
+                causal=False,
+            )
+        self.mlp = SwiGLU(hidden_size=config.hidden_size, expansion=config.expansion)
+        self.norm_eps = config.rms_norm_eps
+
+    def forward(self, cos_sin: CosSin, hidden_states: torch.Tensor) -> torch.Tensor:
+        if self.config.mlp_t:
+            # MLP over sequence dimension (mlp-t)
+            hidden_states = hidden_states.transpose(1, 2)
+            out = self.mlp_t(hidden_states)
+            hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+            hidden_states = hidden_states.transpose(1, 2)
+        else:
+            hidden_states = rms_norm(
+                hidden_states + self.self_attn(cos_sin=cos_sin, hidden_states=hidden_states),
+                variance_epsilon=self.norm_eps,
+            )
+        out = self.mlp(hidden_states)
+        hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+        return hidden_states
+
+class GLPSReasoningModule(nn.Module):
+    """Reasoning stack with optional masked updates (only update uncertain tokens)."""
+    def __init__(self, layers: List[GLPSBlock]):
+        super().__init__()
+        self.layers = torch.nn.ModuleList(layers)
+
+    def forward(self, hidden_states: torch.Tensor, input_injection: torch.Tensor, update_mask: torch.Tensor = None, **kwargs) -> torch.Tensor:
+        x = hidden_states
+        for layer in self.layers:
+            # Compute candidate update using injected context
+            y = layer(hidden_states=x + input_injection, **kwargs)
+            if update_mask is not None:
+                # Convex blend keeps frozen tokens unchanged
+                m = update_mask.to(x.dtype)[..., None]
+                x = x + m * (y - x)
+            else:
+                x = y
+        return x
+
+class GLPS_ACTV1_Inner(nn.Module):
+    def __init__(self, config: GLPS_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        self.forward_dtype = getattr(torch, self.config.forward_dtype)
+
+        # I/O
+        self.embed_scale  = math.sqrt(self.config.hidden_size)
+        embed_init_std = 1.0 / self.embed_scale
+
+        self.embed_tokens = CastedEmbedding(self.config.vocab_size, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+        self.lm_head      = CastedLinear(self.config.hidden_size, self.config.vocab_size, bias=False)
+        self.q_head       = CastedLinear(self.config.hidden_size, 2, bias=True)
+
+        # Puzzle emb (optional) — same convention as HRM/TRM
+        self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size)  # ceil div
+        if self.config.puzzle_emb_ndim > 0:
+            self.puzzle_emb = CastedSparseEmbedding(self.config.num_puzzle_identifiers, self.config.puzzle_emb_ndim, batch_size=self.config.batch_size, init_std=0, cast_to=self.forward_dtype)
+
+        # Positional encodings
+        if self.config.pos_encodings == "rope":
+            self.rotary_emb = RotaryEmbedding(dim=self.config.hidden_size // self.config.num_heads, max_position_embeddings=self.config.seq_len + self.puzzle_emb_len, base=self.config.rope_theta)
+        elif self.config.pos_encodings == "learned":
+            self.embed_pos = CastedEmbedding(self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+
+        # Reasoning stacks
+        self.H_level = GLPSReasoningModule(layers=[GLPSBlock(self.config) for _ in range(self.config.H_layers)])
+        self.L_level = GLPSReasoningModule(layers=[GLPSBlock(self.config) for _ in range(self.config.L_layers)])
+
+        # Initial states (match HRM/TRM style)
+        H_init = trunc_normal_init_(torch.empty(1, 1, self.config.hidden_size, dtype=self.forward_dtype), std=1.0)
+        L_init = trunc_normal_init_(torch.empty(1, 1, self.config.hidden_size, dtype=self.forward_dtype), std=1.0)
+        self.register_buffer("H_init", H_init, persistent=True)
+        self.register_buffer("L_init", L_init, persistent=True)
+
+        # GLPS small heads
+        self.candidate_head = CastedLinear(self.config.hidden_size, 16, bias=True)  # task-specific; for Sudoku you can slice to 9
+        self.certainty_head = CastedLinear(self.config.hidden_size, 1, bias=True)
+        self.energy_head    = CastedLinear(self.config.hidden_size, 1, bias=True)
+
+        # Low-rank dependency scorer (shared)
+        r = max(1, self.config.dep_rank)
+        self.dep_q = CastedLinear(self.config.hidden_size, r, bias=False)
+        self.dep_k = CastedLinear(self.config.hidden_size, r, bias=False)
+
+        # Q head init like HRM/TRM (near-zero -> easier bootstrapping)
+        with torch.no_grad():
+            self.q_head.weight.zero_()
+            self.q_head.bias.fill_(-5)
+
+    def _input_embeddings(self, input: torch.Tensor, puzzle_identifiers: torch.Tensor):
+        # Token embedding
+        embedding = self.embed_tokens(input.to(torch.int32))
+
+        # Puzzle embeddings
+        if self.config.puzzle_emb_ndim > 0:
+            puzzle_embedding = self.puzzle_emb(puzzle_identifiers)
+            pad_count = self.puzzle_emb_len * self.config.hidden_size - puzzle_embedding.shape[-1]
+            if pad_count > 0:
+                puzzle_embedding = F.pad(puzzle_embedding, (0, pad_count))
+            embedding = torch.cat((puzzle_embedding.view(-1, self.puzzle_emb_len, self.config.hidden_size), embedding), dim=-2)
+
+        # Position embeddings
+        if self.config.pos_encodings == "learned":
+            embedding = 0.707106781 * (embedding + self.embed_pos.embedding_weight.to(self.forward_dtype))
+
+        return self.embed_scale * embedding
+
+    def empty_carry(self, batch_size: int):
+        return GLPS_ACTV1InnerCarry(
+            z_H=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+            z_L=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+        )
+
+    def reset_carry(self, reset_flag: torch.Tensor, carry: GLPS_ACTV1InnerCarry):
+        # Explicitly expand buffers and mask to target shapes to avoid shape confusion
+        B, L, D = carry.z_H.shape
+        # Reduce/reset flag to per-batch boolean vector of shape [B]
+        if reset_flag.ndim == 1 and reset_flag.shape[0] == B:
+            reset_b = reset_flag.to(torch.bool)
+        else:
+            # If shape is [B, ...] reduce across non-batch dims; otherwise fallback to first B entries
+            try:
+                reset_b = reset_flag.reshape(B, -1).any(dim=1).to(torch.bool)
+            except Exception:
+                reset_b = reset_flag.reshape(-1)[:B].to(torch.bool)
+        m = reset_b.view(B, 1, 1)
+        mH = m.expand(B, L, D)
+        mL = mH  # same shape for z_L
+        H_init_exp = self.H_init.expand(B, L, D)
+        L_init_exp = self.L_init.expand(B, L, D)
+        return GLPS_ACTV1InnerCarry(
+            z_H=torch.where(mH, H_init_exp, carry.z_H),
+            z_L=torch.where(mL, L_init_exp, carry.z_L),
+        )
+
+    def _global_scan(self, z_L, z_H, input_embeddings, seq_info):
+        # One light pass to gather global signals
+        z_scan = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+        cand_logits = self.candidate_head(z_scan)              # [B, L, C]
+        certainty   = torch.sigmoid(self.certainty_head(z_scan))  # [B, L, 1]
+        return z_scan, cand_logits, certainty
+
+    def _build_dep_mask(self, z_ctx, uncertain_mask: torch.Tensor):
+        """Compute a dependency-based focus mask from a low-rank bilinear score.
+        uncertain_mask: [B, L] boolean mask of cells that are currently uncertain
+        Returns: dep_mask [B, L] boolean mask of cells to (re)update.
+        """
+        B, L, D = z_ctx.shape
+        # Project to low-rank space; use float32 to avoid dtype mismatches under torch.compile
+        Q = self.dep_q(z_ctx).to(torch.float32)   # [B, L, r]
+        K = self.dep_k(z_ctx).to(torch.float32)   # [B, L, r]
+        r = max(1, int(Q.shape[-1]))
+        sim = torch.matmul(Q, K.transpose(1, 2))  # [B, L, L] (float32)
+        sim = sim / math.sqrt(r)
+
+        # Aggregate influence from uncertain queries onto target tokens
+        src = uncertain_mask.to(sim.dtype).unsqueeze(1)      # [B, 1, L]
+        influence = torch.matmul(src, sim).squeeze(1)        # [B, L]
+
+        # Top-k influenced tokens per batch
+        topk = min(self.config.dep_topk, L)
+        vals, idx = torch.topk(influence, k=topk, dim=-1)    # [B, topk]
+        dep_mask = torch.zeros_like(uncertain_mask)
+        dep_mask.scatter_(1, idx, True)
+
+        # Always include uncertain cells themselves
+        dep_mask = dep_mask | uncertain_mask
+        return dep_mask
+
+    def forward(self, carry: GLPS_ACTV1InnerCarry, batch: Dict[str, torch.Tensor]):
+        seq_info = dict(cos_sin=getattr(self, "rotary_emb", None)() if hasattr(self, "rotary_emb") else None)
+
+        # Encode inputs
+        input_embeddings = self._input_embeddings(batch["inputs"], batch["puzzle_identifiers"])
+
+        # States
+        z_H, z_L = carry.z_H, carry.z_L
+
+        if not self.config.glps_enabled:
+            # Fallback to an HRM-like single-cycle grad update for compatibility
+            with torch.no_grad():
+                for _H in range(self.config.H_cycles - 1):
+                    for _L in range(self.config.L_cycles):
+                        z_L = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+                    z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+            # final grad step
+            for _L in range(self.config.L_cycles):
+                z_L = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+            z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+
+            # Outputs
+            new_carry = GLPS_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach())
+            logits = self.lm_head(z_H)[:, self.puzzle_emb_len:]
+            q_logits = self.q_head(z_H[:, 0]).to(torch.float32)
+            conf = torch.zeros_like(q_logits[..., :1]) + 0.5  # neutral
+            return new_carry, logits, (q_logits[..., 0], q_logits[..., 1]), conf
+
+        # ===== GLPS path =====
+        # H1: global scan (cheap)
+        with torch.no_grad():
+            z_scan, cand_logits, certainty = self._global_scan(z_L, z_H, input_embeddings, seq_info)
+
+        # L1: fill-obvious -> compute stable vs uncertain masks
+        if self.config.glps_fill_obvious:
+            obvious_mask = (certainty >= self.config.glps_tau_uncertain)  # [B, L, 1]
+        else:
+            obvious_mask = torch.zeros_like(certainty).bool()
+        stable_mask = obvious_mask.squeeze(-1)         # [B, L]
+        uncertain_mask = ~stable_mask                  # [B, L]
+
+        # H2: dependency prediction over remaining cells
+        if self.config.glps_dep_graph:
+            dep_mask = self._build_dep_mask(z_scan, uncertain_mask)   # [B, L]
+        else:
+            dep_mask = uncertain_mask
+
+        # L2: targeted refinement (a couple of masked iters)
+        update_mask = dep_mask if self.config.glps_token_masking else None
+        z = z_scan.detach()  # use scanned context as start
+        for _ in range(self.config.glps_max_targeted_iters):
+            z = self.L_level(z, z_H + input_embeddings, update_mask=update_mask, **seq_info)
+            # Refresh certainty to shrink mask (optional but cheap)
+            cert_now = torch.sigmoid(self.certainty_head(z)).squeeze(-1)
+            if self.config.glps_token_masking:
+                update_mask = dep_mask & (cert_now < self.config.glps_tau_uncertain)
+
+        # Merge into H and do a light H update with grad
+        z_L = z
+        z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+
+        # H3: energy/consistency -> confidence & optional global propagate
+        energy = torch.sigmoid(self.energy_head(z_H)).mean(dim=1)  # [B, 1]
+        conf = 1.0 - energy
+        need_sweep = (conf.squeeze(-1) < self.config.glps_tau_halt)
+        if self.config.glps_global_propagate_on_low_conf and need_sweep.any():
+            # one final full sweep only for rows needing it
+            maskB = need_sweep.view(-1, 1, 1)
+            zL2 = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+            zH2 = self.H_level(z_H, zL2, update_mask=None, **seq_info)
+            z_L = torch.where(maskB, zL2, z_L)
+            z_H = torch.where(maskB, zH2, z_H)
+
+        # Outputs
+        new_carry = GLPS_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach())
+        logits = self.lm_head(z_H)[:, self.puzzle_emb_len:]
+        q_logits = self.q_head(z_H[:, 0]).to(torch.float32)
+        return new_carry, logits, (q_logits[..., 0], q_logits[..., 1]), conf
+
+class GLPS_ACTV1(nn.Module):
+    """ACT-style wrapper that mixes Q-halt with GLPS confidence."""
+    def __init__(self, config_dict: dict):
+        super().__init__()
+        self.config = GLPS_ACTV1Config(**config_dict)
+        self.inner = GLPS_ACTV1_Inner(self.config)
+
+    @property
+    def puzzle_emb(self):
+        return self.inner.puzzle_emb
+
+    def initial_carry(self, batch: Dict[str, torch.Tensor]):
+        batch_size = batch["inputs"].shape[0]
+        return GLPS_ACTV1Carry(
+            inner_carry=self.inner.empty_carry(batch_size),
+            steps=torch.zeros((batch_size,), dtype=torch.int32),
+            halted=torch.ones((batch_size,), dtype=torch.bool),  # start halted to force reset on first pass
+            current_data={k: torch.empty_like(v) for k, v in batch.items()}
+        )
+
+    def forward(self, carry: GLPS_ACTV1Carry, batch: Dict[str, torch.Tensor]):
+        # Reset halted seqs
+        new_inner_carry = self.inner.reset_carry(carry.halted, carry.inner_carry)
+        new_steps = torch.where(carry.halted, 0, carry.steps)
+        new_current_data = {k: torch.where(carry.halted.view((-1,) + (1,) * (batch[k].ndim - 1)), batch[k], v) for k, v in carry.current_data.items()}
+
+        # Inner step
+        new_inner_carry, logits, (q_halt_logits, q_continue_logits), conf = self.inner(new_inner_carry, new_current_data)
+
+        outputs = {
+            "logits": logits,
+            "q_halt_logits": q_halt_logits,
+            "q_continue_logits": q_continue_logits,
+            "conf": conf.squeeze(-1),
+        }
+
+        with torch.no_grad():
+            new_steps = new_steps + 1
+            is_last_step = new_steps >= self.config.halt_max_steps
+
+            # Combine halt signals: Q or confidence or last-step
+            halted = is_last_step | (q_halt_logits > q_continue_logits) | (conf.squeeze(-1) >= self.config.glps_tau_halt)
+
+            # Exploration during training only
+            if self.training and (self.config.halt_max_steps > 1):
+                min_halt_steps = (torch.rand_like(q_halt_logits) < self.config.halt_exploration_prob) * torch.randint_like(new_steps, low=2, high=self.config.halt_max_steps + 1)
+                halted = halted & (new_steps >= min_halt_steps)
+                
+                # Optional target for Q-learning (kept similar to HRM)
+                next_conf = self.inner(new_inner_carry, new_current_data)[-1]
+                outputs["target_q_continue"] = torch.sigmoid(torch.where(is_last_step, q_halt_logits, torch.maximum(q_halt_logits, q_continue_logits)))
+            else:
+                # During eval, always use max_steps to ensure consistent reasoning depth (same as TRM/HRM eval behavior)
+                halted = is_last_step
+
+        return GLPS_ACTV1Carry(new_inner_carry, new_steps, halted, new_current_data), outputs
diff --git a/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_cos/losses.py b/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_cos/losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..cdfb77718bc16f98cdd7da06eb5795c36b866aa7
--- /dev/null
+++ b/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_cos/losses.py
@@ -0,0 +1,102 @@
+from typing import Any, Tuple, Dict, Sequence, Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+import math
+
+IGNORE_LABEL_ID = -100
+
+
+def s(x, epsilon=1e-30):
+    return torch.where(
+        x<0,
+        1/(1-x+ epsilon),
+        x + 1
+    )
+
+
+def log_stablemax(x, dim=-1):
+    s_x = s(x)
+    return torch.log(s_x/torch.sum(s_x, dim=dim, keepdim=True))
+
+
+def stablemax_cross_entropy(logits, labels, ignore_index: int = -100, valid_mask=None):
+    logprobs = log_stablemax(logits.to(torch.float64), dim=-1)
+
+    if valid_mask is None:
+        valid_mask = (labels != ignore_index)
+    transformed_labels = torch.where(valid_mask, labels, 0)
+    prediction_logprobs = torch.gather(logprobs, index=transformed_labels.to(torch.long).unsqueeze(-1), dim=-1).squeeze(-1)
+
+    return -torch.where(valid_mask, prediction_logprobs, 0)
+
+
+def softmax_cross_entropy(logits, labels, ignore_index: int = -100):
+    # Cast logits to f32
+    # Flatten logits
+    return F.cross_entropy(logits.to(torch.float32).view(-1, logits.shape[-1]), labels.to(torch.long).view(-1), ignore_index=ignore_index, reduction="none").view(labels.shape)
+
+
+class ACTLossHead(nn.Module):
+    def __init__(self, model: nn.Module, loss_type: str):
+        super().__init__()
+        self.model = model
+        self.loss_fn = globals()[loss_type]
+        
+    def initial_carry(self, *args, **kwargs):
+        return self.model.initial_carry(*args, **kwargs)  # type: ignore
+
+    def forward(
+        self,
+        return_keys: Sequence[str],
+        # Model args
+        **model_kwargs,
+    ) -> Tuple[Any, torch.Tensor, Dict[str, torch.Tensor], Optional[Dict[str, torch.Tensor]], torch.Tensor]:
+        # Model logits
+        # B x SeqLen x D
+        new_carry, outputs = self.model(**model_kwargs)
+        labels = new_carry.current_data["labels"]
+
+        with torch.no_grad():
+            # Preds
+            outputs["preds"] = torch.argmax(outputs["logits"], dim=-1)
+
+            # Correctness
+            mask = (labels != IGNORE_LABEL_ID)
+            loss_counts = mask.sum(-1)
+            loss_divisor = loss_counts.clamp_min(1).unsqueeze(-1)  # Avoid NaNs in division
+
+            is_correct = mask & (torch.argmax(outputs["logits"], dim=-1) == labels)
+            seq_is_correct = is_correct.sum(-1) == loss_counts
+            
+            # Metrics (halted)
+            valid_metrics = new_carry.halted & (loss_counts > 0)
+            metrics = {
+                "count": valid_metrics.sum(),
+                
+                "accuracy":       torch.where(valid_metrics, (is_correct.to(torch.float32) / loss_divisor).sum(-1), 0).sum(),
+                "exact_accuracy": (valid_metrics & seq_is_correct).sum(),
+
+                "q_halt_accuracy": (valid_metrics & ((outputs["q_halt_logits"] >= 0) == seq_is_correct)).sum(),
+                "steps":          torch.where(valid_metrics, new_carry.steps, 0).sum(),
+            }
+
+        # Losses
+
+        lm_loss = (self.loss_fn(outputs["logits"], labels, ignore_index=IGNORE_LABEL_ID, valid_mask=mask) / loss_divisor).sum()
+        q_halt_loss = F.binary_cross_entropy_with_logits(outputs["q_halt_logits"], seq_is_correct.to(outputs["q_halt_logits"].dtype), reduction="sum")
+        metrics.update({
+            "lm_loss": lm_loss.detach(),
+            "q_halt_loss": q_halt_loss.detach(),
+        })
+        # Q continue (bootstrapping target loss); Alexia: This fits Q-learning, but seems totally unecessary
+        q_continue_loss = 0
+        if "target_q_continue" in outputs:
+            q_continue_loss = F.binary_cross_entropy_with_logits(outputs["q_continue_logits"], outputs["target_q_continue"], reduction="sum")
+
+            metrics["q_continue_loss"] = q_continue_loss.detach()
+        # Filter outputs for return
+        detached_outputs = {k: outputs[k].detach() for k in return_keys if k in outputs}
+
+        return new_carry, lm_loss + 0.5 * (q_halt_loss + q_continue_loss), metrics, detached_outputs, new_carry.halted.all()
diff --git a/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_cos_b160/all_config.yaml b/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_cos_b160/all_config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..c5d4646ae84acbfd889e83dc5804ebbee7c3d40d
--- /dev/null
+++ b/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_cos_b160/all_config.yaml
@@ -0,0 +1,60 @@
+arch:
+  H_cycles: 3
+  H_layers: 2
+  L_cycles: 1
+  L_layers: 7
+  dep_rank: 64
+  dep_topk: 12
+  expansion: 4.0
+  forward_dtype: bfloat16
+  glps_dep_graph: true
+  glps_enabled: true
+  glps_fill_obvious: true
+  glps_global_propagate_on_low_conf: true
+  glps_max_targeted_iters: 4
+  glps_tau_halt: 0.95
+  glps_tau_uncertain: 0.8
+  glps_token_masking: true
+  halt_exploration_prob: 0.1
+  halt_max_steps: 16
+  hidden_size: 512
+  loss:
+    loss_type: stablemax_cross_entropy
+    name: losses@ACTLossHead
+  mlp_t: false
+  name: recursive_reasoning.glps@GLPS_ACTV1
+  num_heads: 8
+  pos_encodings: rope
+  puzzle_emb_ndim: 0
+  rms_norm_eps: 1.0e-05
+  rope_theta: 10000.0
+beta1: 0.9
+beta2: 0.95
+checkpoint_every_eval: true
+checkpoint_path: checkpoints/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_cos_b160
+data_paths:
+- data/maze-30x30-hard-1k
+data_paths_test: []
+ema: true
+ema_rate: 0.999
+epochs: 50000
+eval_glps_max_targeted_iters: null
+eval_glps_tau_halt: null
+eval_halt_max_steps: null
+eval_interval: 5000
+eval_only: false
+eval_save_outputs: []
+evaluators: []
+freeze_weights: false
+global_batch_size: 160
+load_checkpoint: null
+lr: 0.0001
+lr_min_ratio: 0.1
+lr_warmup_steps: 2000
+min_eval_interval: 0
+project_name: Maze-30x30-hard-1k-ACT-torch
+puzzle_emb_lr: 0.0001
+puzzle_emb_weight_decay: 1.0
+run_name: pretrain_glps_maze30_v1_cos_b160
+seed: 0
+weight_decay: 1.0
diff --git a/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_cos_b160/glps.py b/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_cos_b160/glps.py
new file mode 100644
index 0000000000000000000000000000000000000000..96e6de932e79e174cf773779293a643842e8a268
--- /dev/null
+++ b/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_cos_b160/glps.py
@@ -0,0 +1,409 @@
+
+from typing import Tuple, List, Dict
+from dataclasses import dataclass
+import math
+import torch
+import torch.nn.functional as F
+from torch import nn
+from pydantic import BaseModel
+
+# Reuse the same building blocks as HRM/TRM
+from models.common import trunc_normal_init_
+from models.layers import rms_norm, SwiGLU, Attention, RotaryEmbedding, CosSin, CastedEmbedding, CastedLinear
+from models.sparse_embedding import CastedSparseEmbedding
+
+"""
+Global-Local Predictive Solver (GLPS)
+------------------------------------
+A light-weight control-policy on top of the HRM/TRM style blocks:
+- H1: global scan -> certainty map
+- L1: fill-obvious (lock stable cells)
+- H2: dependency scoring over remaining cells
+- L2: targeted refinement (masked updates)
+- H3: energy-based confidence -> (optional) one global propagate sweep -> halt
+
+This file keeps parameter growth tiny: a few heads + (optional) low-rank dependency scorer.
+"""
+
+@dataclass
+class GLPS_ACTV1InnerCarry:
+    z_H: torch.Tensor
+    z_L: torch.Tensor
+
+@dataclass
+class GLPS_ACTV1Carry:
+    inner_carry: GLPS_ACTV1InnerCarry
+    steps: torch.Tensor
+    halted: torch.Tensor
+    current_data: Dict[str, torch.Tensor]
+
+class GLPS_ACTV1Config(BaseModel):
+    # Core IO / shapes
+    batch_size: int
+    seq_len: int
+    puzzle_emb_ndim: int = 0
+    num_puzzle_identifiers: int = 1
+    vocab_size: int = 256
+
+    # Cycle schedule
+    H_cycles: int = 3              # (scan -> refine -> check) typical
+    L_cycles: int = 1
+
+    # Depth
+    H_layers: int = 2
+    L_layers: int = 4
+
+    # Transformer config
+    hidden_size: int = 512
+    expansion: float = 2.0
+    num_heads: int = 8
+    pos_encodings: str = "rope"
+
+    rms_norm_eps: float = 1e-5
+    rope_theta: float = 10000.0
+
+    # ACT wrapper
+    halt_max_steps: int = 4
+    halt_exploration_prob: float = 0.1
+
+    forward_dtype: str = "bfloat16"
+
+    # Optional: use MLP on L instead of attention (matches HRM/TRM option)
+    mlp_t: bool = False
+
+    # ---- GLPS extras (tiny) ----
+    glps_enabled: bool = True
+    glps_fill_obvious: bool = True
+    glps_dep_graph: bool = True
+    glps_token_masking: bool = True
+    glps_global_propagate_on_low_conf: bool = True
+
+    glps_tau_halt: float = 0.95       # final confidence to halt
+    glps_tau_uncertain: float = 0.60  # cell-wise certainty threshold
+    glps_max_targeted_iters: int = 2  # small number: 1-2
+
+    # Dependency scorer (low rank bilinear)
+    dep_rank: int = 32
+    dep_topk: int = 8
+
+class GLPSBlock(nn.Module):
+    def __init__(self, config: GLPS_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        if self.config.mlp_t:
+            self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size)
+            self.mlp_t = SwiGLU(
+                hidden_size=self.config.seq_len + self.puzzle_emb_len, # treat sequence as channel
+                expansion=config.expansion,
+            )
+        else:
+            self.self_attn = Attention(
+                hidden_size=config.hidden_size,
+                head_dim=config.hidden_size // config.num_heads,
+                num_heads=config.num_heads,
+                num_key_value_heads=config.num_heads,
+                causal=False,
+            )
+        self.mlp = SwiGLU(hidden_size=config.hidden_size, expansion=config.expansion)
+        self.norm_eps = config.rms_norm_eps
+
+    def forward(self, cos_sin: CosSin, hidden_states: torch.Tensor) -> torch.Tensor:
+        if self.config.mlp_t:
+            # MLP over sequence dimension (mlp-t)
+            hidden_states = hidden_states.transpose(1, 2)
+            out = self.mlp_t(hidden_states)
+            hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+            hidden_states = hidden_states.transpose(1, 2)
+        else:
+            hidden_states = rms_norm(
+                hidden_states + self.self_attn(cos_sin=cos_sin, hidden_states=hidden_states),
+                variance_epsilon=self.norm_eps,
+            )
+        out = self.mlp(hidden_states)
+        hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+        return hidden_states
+
+class GLPSReasoningModule(nn.Module):
+    """Reasoning stack with optional masked updates (only update uncertain tokens)."""
+    def __init__(self, layers: List[GLPSBlock]):
+        super().__init__()
+        self.layers = torch.nn.ModuleList(layers)
+
+    def forward(self, hidden_states: torch.Tensor, input_injection: torch.Tensor, update_mask: torch.Tensor = None, **kwargs) -> torch.Tensor:
+        x = hidden_states
+        for layer in self.layers:
+            # Compute candidate update using injected context
+            y = layer(hidden_states=x + input_injection, **kwargs)
+            if update_mask is not None:
+                # Convex blend keeps frozen tokens unchanged
+                m = update_mask.to(x.dtype)[..., None]
+                x = x + m * (y - x)
+            else:
+                x = y
+        return x
+
+class GLPS_ACTV1_Inner(nn.Module):
+    def __init__(self, config: GLPS_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        self.forward_dtype = getattr(torch, self.config.forward_dtype)
+
+        # I/O
+        self.embed_scale  = math.sqrt(self.config.hidden_size)
+        embed_init_std = 1.0 / self.embed_scale
+
+        self.embed_tokens = CastedEmbedding(self.config.vocab_size, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+        self.lm_head      = CastedLinear(self.config.hidden_size, self.config.vocab_size, bias=False)
+        self.q_head       = CastedLinear(self.config.hidden_size, 2, bias=True)
+
+        # Puzzle emb (optional) — same convention as HRM/TRM
+        self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size)  # ceil div
+        if self.config.puzzle_emb_ndim > 0:
+            self.puzzle_emb = CastedSparseEmbedding(self.config.num_puzzle_identifiers, self.config.puzzle_emb_ndim, batch_size=self.config.batch_size, init_std=0, cast_to=self.forward_dtype)
+
+        # Positional encodings
+        if self.config.pos_encodings == "rope":
+            self.rotary_emb = RotaryEmbedding(dim=self.config.hidden_size // self.config.num_heads, max_position_embeddings=self.config.seq_len + self.puzzle_emb_len, base=self.config.rope_theta)
+        elif self.config.pos_encodings == "learned":
+            self.embed_pos = CastedEmbedding(self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+
+        # Reasoning stacks
+        self.H_level = GLPSReasoningModule(layers=[GLPSBlock(self.config) for _ in range(self.config.H_layers)])
+        self.L_level = GLPSReasoningModule(layers=[GLPSBlock(self.config) for _ in range(self.config.L_layers)])
+
+        # Initial states (match HRM/TRM style)
+        H_init = trunc_normal_init_(torch.empty(1, 1, self.config.hidden_size, dtype=self.forward_dtype), std=1.0)
+        L_init = trunc_normal_init_(torch.empty(1, 1, self.config.hidden_size, dtype=self.forward_dtype), std=1.0)
+        self.register_buffer("H_init", H_init, persistent=True)
+        self.register_buffer("L_init", L_init, persistent=True)
+
+        # GLPS small heads
+        self.candidate_head = CastedLinear(self.config.hidden_size, 16, bias=True)  # task-specific; for Sudoku you can slice to 9
+        self.certainty_head = CastedLinear(self.config.hidden_size, 1, bias=True)
+        self.energy_head    = CastedLinear(self.config.hidden_size, 1, bias=True)
+
+        # Low-rank dependency scorer (shared)
+        r = max(1, self.config.dep_rank)
+        self.dep_q = CastedLinear(self.config.hidden_size, r, bias=False)
+        self.dep_k = CastedLinear(self.config.hidden_size, r, bias=False)
+
+        # Q head init like HRM/TRM (near-zero -> easier bootstrapping)
+        with torch.no_grad():
+            self.q_head.weight.zero_()
+            self.q_head.bias.fill_(-5)
+
+    def _input_embeddings(self, input: torch.Tensor, puzzle_identifiers: torch.Tensor):
+        # Token embedding
+        embedding = self.embed_tokens(input.to(torch.int32))
+
+        # Puzzle embeddings
+        if self.config.puzzle_emb_ndim > 0:
+            puzzle_embedding = self.puzzle_emb(puzzle_identifiers)
+            pad_count = self.puzzle_emb_len * self.config.hidden_size - puzzle_embedding.shape[-1]
+            if pad_count > 0:
+                puzzle_embedding = F.pad(puzzle_embedding, (0, pad_count))
+            embedding = torch.cat((puzzle_embedding.view(-1, self.puzzle_emb_len, self.config.hidden_size), embedding), dim=-2)
+
+        # Position embeddings
+        if self.config.pos_encodings == "learned":
+            embedding = 0.707106781 * (embedding + self.embed_pos.embedding_weight.to(self.forward_dtype))
+
+        return self.embed_scale * embedding
+
+    def empty_carry(self, batch_size: int):
+        return GLPS_ACTV1InnerCarry(
+            z_H=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+            z_L=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+        )
+
+    def reset_carry(self, reset_flag: torch.Tensor, carry: GLPS_ACTV1InnerCarry):
+        # Explicitly expand buffers and mask to target shapes to avoid shape confusion
+        B, L, D = carry.z_H.shape
+        # Reduce/reset flag to per-batch boolean vector of shape [B]
+        if reset_flag.ndim == 1 and reset_flag.shape[0] == B:
+            reset_b = reset_flag.to(torch.bool)
+        else:
+            # If shape is [B, ...] reduce across non-batch dims; otherwise fallback to first B entries
+            try:
+                reset_b = reset_flag.reshape(B, -1).any(dim=1).to(torch.bool)
+            except Exception:
+                reset_b = reset_flag.reshape(-1)[:B].to(torch.bool)
+        m = reset_b.view(B, 1, 1)
+        mH = m.expand(B, L, D)
+        mL = mH  # same shape for z_L
+        H_init_exp = self.H_init.expand(B, L, D)
+        L_init_exp = self.L_init.expand(B, L, D)
+        return GLPS_ACTV1InnerCarry(
+            z_H=torch.where(mH, H_init_exp, carry.z_H),
+            z_L=torch.where(mL, L_init_exp, carry.z_L),
+        )
+
+    def _global_scan(self, z_L, z_H, input_embeddings, seq_info):
+        # One light pass to gather global signals
+        z_scan = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+        cand_logits = self.candidate_head(z_scan)              # [B, L, C]
+        certainty   = torch.sigmoid(self.certainty_head(z_scan))  # [B, L, 1]
+        return z_scan, cand_logits, certainty
+
+    def _build_dep_mask(self, z_ctx, uncertain_mask: torch.Tensor):
+        """Compute a dependency-based focus mask from a low-rank bilinear score.
+        uncertain_mask: [B, L] boolean mask of cells that are currently uncertain
+        Returns: dep_mask [B, L] boolean mask of cells to (re)update.
+        """
+        B, L, D = z_ctx.shape
+        # Project to low-rank space; use float32 to avoid dtype mismatches under torch.compile
+        Q = self.dep_q(z_ctx).to(torch.float32)   # [B, L, r]
+        K = self.dep_k(z_ctx).to(torch.float32)   # [B, L, r]
+        r = max(1, int(Q.shape[-1]))
+        sim = torch.matmul(Q, K.transpose(1, 2))  # [B, L, L] (float32)
+        sim = sim / math.sqrt(r)
+
+        # Aggregate influence from uncertain queries onto target tokens
+        src = uncertain_mask.to(sim.dtype).unsqueeze(1)      # [B, 1, L]
+        influence = torch.matmul(src, sim).squeeze(1)        # [B, L]
+
+        # Top-k influenced tokens per batch
+        topk = min(self.config.dep_topk, L)
+        vals, idx = torch.topk(influence, k=topk, dim=-1)    # [B, topk]
+        dep_mask = torch.zeros_like(uncertain_mask)
+        dep_mask.scatter_(1, idx, True)
+
+        # Always include uncertain cells themselves
+        dep_mask = dep_mask | uncertain_mask
+        return dep_mask
+
+    def forward(self, carry: GLPS_ACTV1InnerCarry, batch: Dict[str, torch.Tensor]):
+        seq_info = dict(cos_sin=getattr(self, "rotary_emb", None)() if hasattr(self, "rotary_emb") else None)
+
+        # Encode inputs
+        input_embeddings = self._input_embeddings(batch["inputs"], batch["puzzle_identifiers"])
+
+        # States
+        z_H, z_L = carry.z_H, carry.z_L
+
+        if not self.config.glps_enabled:
+            # Fallback to an HRM-like single-cycle grad update for compatibility
+            with torch.no_grad():
+                for _H in range(self.config.H_cycles - 1):
+                    for _L in range(self.config.L_cycles):
+                        z_L = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+                    z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+            # final grad step
+            for _L in range(self.config.L_cycles):
+                z_L = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+            z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+
+            # Outputs
+            new_carry = GLPS_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach())
+            logits = self.lm_head(z_H)[:, self.puzzle_emb_len:]
+            q_logits = self.q_head(z_H[:, 0]).to(torch.float32)
+            conf = torch.zeros_like(q_logits[..., :1]) + 0.5  # neutral
+            return new_carry, logits, (q_logits[..., 0], q_logits[..., 1]), conf
+
+        # ===== GLPS path =====
+        # H1: global scan (cheap)
+        with torch.no_grad():
+            z_scan, cand_logits, certainty = self._global_scan(z_L, z_H, input_embeddings, seq_info)
+
+        # L1: fill-obvious -> compute stable vs uncertain masks
+        if self.config.glps_fill_obvious:
+            obvious_mask = (certainty >= self.config.glps_tau_uncertain)  # [B, L, 1]
+        else:
+            obvious_mask = torch.zeros_like(certainty).bool()
+        stable_mask = obvious_mask.squeeze(-1)         # [B, L]
+        uncertain_mask = ~stable_mask                  # [B, L]
+
+        # H2: dependency prediction over remaining cells
+        if self.config.glps_dep_graph:
+            dep_mask = self._build_dep_mask(z_scan, uncertain_mask)   # [B, L]
+        else:
+            dep_mask = uncertain_mask
+
+        # L2: targeted refinement (a couple of masked iters)
+        update_mask = dep_mask if self.config.glps_token_masking else None
+        z = z_scan.detach()  # use scanned context as start
+        for _ in range(self.config.glps_max_targeted_iters):
+            z = self.L_level(z, z_H + input_embeddings, update_mask=update_mask, **seq_info)
+            # Refresh certainty to shrink mask (optional but cheap)
+            cert_now = torch.sigmoid(self.certainty_head(z)).squeeze(-1)
+            if self.config.glps_token_masking:
+                update_mask = dep_mask & (cert_now < self.config.glps_tau_uncertain)
+
+        # Merge into H and do a light H update with grad
+        z_L = z
+        z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+
+        # H3: energy/consistency -> confidence & optional global propagate
+        energy = torch.sigmoid(self.energy_head(z_H)).mean(dim=1)  # [B, 1]
+        conf = 1.0 - energy
+        need_sweep = (conf.squeeze(-1) < self.config.glps_tau_halt)
+        if self.config.glps_global_propagate_on_low_conf and need_sweep.any():
+            # one final full sweep only for rows needing it
+            maskB = need_sweep.view(-1, 1, 1)
+            zL2 = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+            zH2 = self.H_level(z_H, zL2, update_mask=None, **seq_info)
+            z_L = torch.where(maskB, zL2, z_L)
+            z_H = torch.where(maskB, zH2, z_H)
+
+        # Outputs
+        new_carry = GLPS_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach())
+        logits = self.lm_head(z_H)[:, self.puzzle_emb_len:]
+        q_logits = self.q_head(z_H[:, 0]).to(torch.float32)
+        return new_carry, logits, (q_logits[..., 0], q_logits[..., 1]), conf
+
+class GLPS_ACTV1(nn.Module):
+    """ACT-style wrapper that mixes Q-halt with GLPS confidence."""
+    def __init__(self, config_dict: dict):
+        super().__init__()
+        self.config = GLPS_ACTV1Config(**config_dict)
+        self.inner = GLPS_ACTV1_Inner(self.config)
+
+    @property
+    def puzzle_emb(self):
+        return self.inner.puzzle_emb
+
+    def initial_carry(self, batch: Dict[str, torch.Tensor]):
+        batch_size = batch["inputs"].shape[0]
+        return GLPS_ACTV1Carry(
+            inner_carry=self.inner.empty_carry(batch_size),
+            steps=torch.zeros((batch_size,), dtype=torch.int32),
+            halted=torch.ones((batch_size,), dtype=torch.bool),  # start halted to force reset on first pass
+            current_data={k: torch.empty_like(v) for k, v in batch.items()}
+        )
+
+    def forward(self, carry: GLPS_ACTV1Carry, batch: Dict[str, torch.Tensor]):
+        # Reset halted seqs
+        new_inner_carry = self.inner.reset_carry(carry.halted, carry.inner_carry)
+        new_steps = torch.where(carry.halted, 0, carry.steps)
+        new_current_data = {k: torch.where(carry.halted.view((-1,) + (1,) * (batch[k].ndim - 1)), batch[k], v) for k, v in carry.current_data.items()}
+
+        # Inner step
+        new_inner_carry, logits, (q_halt_logits, q_continue_logits), conf = self.inner(new_inner_carry, new_current_data)
+
+        outputs = {
+            "logits": logits,
+            "q_halt_logits": q_halt_logits,
+            "q_continue_logits": q_continue_logits,
+            "conf": conf.squeeze(-1),
+        }
+
+        with torch.no_grad():
+            new_steps = new_steps + 1
+            is_last_step = new_steps >= self.config.halt_max_steps
+
+            # Combine halt signals: Q or confidence or last-step
+            halted = is_last_step | (q_halt_logits > q_continue_logits) | (conf.squeeze(-1) >= self.config.glps_tau_halt)
+
+            # Exploration during training only
+            if self.training and (self.config.halt_max_steps > 1):
+                min_halt_steps = (torch.rand_like(q_halt_logits) < self.config.halt_exploration_prob) * torch.randint_like(new_steps, low=2, high=self.config.halt_max_steps + 1)
+                halted = halted & (new_steps >= min_halt_steps)
+                
+                # Optional target for Q-learning (kept similar to HRM)
+                next_conf = self.inner(new_inner_carry, new_current_data)[-1]
+                outputs["target_q_continue"] = torch.sigmoid(torch.where(is_last_step, q_halt_logits, torch.maximum(q_halt_logits, q_continue_logits)))
+            else:
+                # During eval, always use max_steps to ensure consistent reasoning depth (same as TRM/HRM eval behavior)
+                halted = is_last_step
+
+        return GLPS_ACTV1Carry(new_inner_carry, new_steps, halted, new_current_data), outputs
diff --git a/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_cos_b160/losses.py b/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_cos_b160/losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..cdfb77718bc16f98cdd7da06eb5795c36b866aa7
--- /dev/null
+++ b/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_cos_b160/losses.py
@@ -0,0 +1,102 @@
+from typing import Any, Tuple, Dict, Sequence, Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+import math
+
+IGNORE_LABEL_ID = -100
+
+
+def s(x, epsilon=1e-30):
+    return torch.where(
+        x<0,
+        1/(1-x+ epsilon),
+        x + 1
+    )
+
+
+def log_stablemax(x, dim=-1):
+    s_x = s(x)
+    return torch.log(s_x/torch.sum(s_x, dim=dim, keepdim=True))
+
+
+def stablemax_cross_entropy(logits, labels, ignore_index: int = -100, valid_mask=None):
+    logprobs = log_stablemax(logits.to(torch.float64), dim=-1)
+
+    if valid_mask is None:
+        valid_mask = (labels != ignore_index)
+    transformed_labels = torch.where(valid_mask, labels, 0)
+    prediction_logprobs = torch.gather(logprobs, index=transformed_labels.to(torch.long).unsqueeze(-1), dim=-1).squeeze(-1)
+
+    return -torch.where(valid_mask, prediction_logprobs, 0)
+
+
+def softmax_cross_entropy(logits, labels, ignore_index: int = -100):
+    # Cast logits to f32
+    # Flatten logits
+    return F.cross_entropy(logits.to(torch.float32).view(-1, logits.shape[-1]), labels.to(torch.long).view(-1), ignore_index=ignore_index, reduction="none").view(labels.shape)
+
+
+class ACTLossHead(nn.Module):
+    def __init__(self, model: nn.Module, loss_type: str):
+        super().__init__()
+        self.model = model
+        self.loss_fn = globals()[loss_type]
+        
+    def initial_carry(self, *args, **kwargs):
+        return self.model.initial_carry(*args, **kwargs)  # type: ignore
+
+    def forward(
+        self,
+        return_keys: Sequence[str],
+        # Model args
+        **model_kwargs,
+    ) -> Tuple[Any, torch.Tensor, Dict[str, torch.Tensor], Optional[Dict[str, torch.Tensor]], torch.Tensor]:
+        # Model logits
+        # B x SeqLen x D
+        new_carry, outputs = self.model(**model_kwargs)
+        labels = new_carry.current_data["labels"]
+
+        with torch.no_grad():
+            # Preds
+            outputs["preds"] = torch.argmax(outputs["logits"], dim=-1)
+
+            # Correctness
+            mask = (labels != IGNORE_LABEL_ID)
+            loss_counts = mask.sum(-1)
+            loss_divisor = loss_counts.clamp_min(1).unsqueeze(-1)  # Avoid NaNs in division
+
+            is_correct = mask & (torch.argmax(outputs["logits"], dim=-1) == labels)
+            seq_is_correct = is_correct.sum(-1) == loss_counts
+            
+            # Metrics (halted)
+            valid_metrics = new_carry.halted & (loss_counts > 0)
+            metrics = {
+                "count": valid_metrics.sum(),
+                
+                "accuracy":       torch.where(valid_metrics, (is_correct.to(torch.float32) / loss_divisor).sum(-1), 0).sum(),
+                "exact_accuracy": (valid_metrics & seq_is_correct).sum(),
+
+                "q_halt_accuracy": (valid_metrics & ((outputs["q_halt_logits"] >= 0) == seq_is_correct)).sum(),
+                "steps":          torch.where(valid_metrics, new_carry.steps, 0).sum(),
+            }
+
+        # Losses
+
+        lm_loss = (self.loss_fn(outputs["logits"], labels, ignore_index=IGNORE_LABEL_ID, valid_mask=mask) / loss_divisor).sum()
+        q_halt_loss = F.binary_cross_entropy_with_logits(outputs["q_halt_logits"], seq_is_correct.to(outputs["q_halt_logits"].dtype), reduction="sum")
+        metrics.update({
+            "lm_loss": lm_loss.detach(),
+            "q_halt_loss": q_halt_loss.detach(),
+        })
+        # Q continue (bootstrapping target loss); Alexia: This fits Q-learning, but seems totally unecessary
+        q_continue_loss = 0
+        if "target_q_continue" in outputs:
+            q_continue_loss = F.binary_cross_entropy_with_logits(outputs["q_continue_logits"], outputs["target_q_continue"], reduction="sum")
+
+            metrics["q_continue_loss"] = q_continue_loss.detach()
+        # Filter outputs for return
+        detached_outputs = {k: outputs[k].detach() for k in return_keys if k in outputs}
+
+        return new_carry, lm_loss + 0.5 * (q_halt_loss + q_continue_loss), metrics, detached_outputs, new_carry.halted.all()
diff --git a/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_e10k_b160/all_config.yaml b/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_e10k_b160/all_config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3e855da6eb9962b35c84bad88fc444d98175c727
--- /dev/null
+++ b/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_e10k_b160/all_config.yaml
@@ -0,0 +1,60 @@
+arch:
+  H_cycles: 3
+  H_layers: 2
+  L_cycles: 1
+  L_layers: 7
+  dep_rank: 64
+  dep_topk: 12
+  expansion: 4.0
+  forward_dtype: bfloat16
+  glps_dep_graph: true
+  glps_enabled: true
+  glps_fill_obvious: true
+  glps_global_propagate_on_low_conf: true
+  glps_max_targeted_iters: 4
+  glps_tau_halt: 0.95
+  glps_tau_uncertain: 0.8
+  glps_token_masking: true
+  halt_exploration_prob: 0.1
+  halt_max_steps: 16
+  hidden_size: 512
+  loss:
+    loss_type: stablemax_cross_entropy
+    name: losses@ACTLossHead
+  mlp_t: false
+  name: recursive_reasoning.glps@GLPS_ACTV1
+  num_heads: 8
+  pos_encodings: rope
+  puzzle_emb_ndim: 0
+  rms_norm_eps: 1.0e-05
+  rope_theta: 10000.0
+beta1: 0.9
+beta2: 0.95
+checkpoint_every_eval: true
+checkpoint_path: checkpoints/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_e10k_b160
+data_paths:
+- data/maze-30x30-hard-1k
+data_paths_test: []
+ema: true
+ema_rate: 0.999
+epochs: 10000
+eval_glps_max_targeted_iters: null
+eval_glps_tau_halt: null
+eval_halt_max_steps: null
+eval_interval: 1000
+eval_only: false
+eval_save_outputs: []
+evaluators: []
+freeze_weights: false
+global_batch_size: 160
+load_checkpoint: null
+lr: 0.0001
+lr_min_ratio: 0.1
+lr_warmup_steps: 2000
+min_eval_interval: 0
+project_name: Maze-30x30-hard-1k-ACT-torch
+puzzle_emb_lr: 0.0001
+puzzle_emb_weight_decay: 1.0
+run_name: pretrain_glps_maze30_v1_e10k_b160
+seed: 0
+weight_decay: 1.0
diff --git a/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_e10k_b160/glps.py b/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_e10k_b160/glps.py
new file mode 100644
index 0000000000000000000000000000000000000000..96e6de932e79e174cf773779293a643842e8a268
--- /dev/null
+++ b/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_e10k_b160/glps.py
@@ -0,0 +1,409 @@
+
+from typing import Tuple, List, Dict
+from dataclasses import dataclass
+import math
+import torch
+import torch.nn.functional as F
+from torch import nn
+from pydantic import BaseModel
+
+# Reuse the same building blocks as HRM/TRM
+from models.common import trunc_normal_init_
+from models.layers import rms_norm, SwiGLU, Attention, RotaryEmbedding, CosSin, CastedEmbedding, CastedLinear
+from models.sparse_embedding import CastedSparseEmbedding
+
+"""
+Global-Local Predictive Solver (GLPS)
+------------------------------------
+A light-weight control-policy on top of the HRM/TRM style blocks:
+- H1: global scan -> certainty map
+- L1: fill-obvious (lock stable cells)
+- H2: dependency scoring over remaining cells
+- L2: targeted refinement (masked updates)
+- H3: energy-based confidence -> (optional) one global propagate sweep -> halt
+
+This file keeps parameter growth tiny: a few heads + (optional) low-rank dependency scorer.
+"""
+
+@dataclass
+class GLPS_ACTV1InnerCarry:
+    z_H: torch.Tensor
+    z_L: torch.Tensor
+
+@dataclass
+class GLPS_ACTV1Carry:
+    inner_carry: GLPS_ACTV1InnerCarry
+    steps: torch.Tensor
+    halted: torch.Tensor
+    current_data: Dict[str, torch.Tensor]
+
+class GLPS_ACTV1Config(BaseModel):
+    # Core IO / shapes
+    batch_size: int
+    seq_len: int
+    puzzle_emb_ndim: int = 0
+    num_puzzle_identifiers: int = 1
+    vocab_size: int = 256
+
+    # Cycle schedule
+    H_cycles: int = 3              # (scan -> refine -> check) typical
+    L_cycles: int = 1
+
+    # Depth
+    H_layers: int = 2
+    L_layers: int = 4
+
+    # Transformer config
+    hidden_size: int = 512
+    expansion: float = 2.0
+    num_heads: int = 8
+    pos_encodings: str = "rope"
+
+    rms_norm_eps: float = 1e-5
+    rope_theta: float = 10000.0
+
+    # ACT wrapper
+    halt_max_steps: int = 4
+    halt_exploration_prob: float = 0.1
+
+    forward_dtype: str = "bfloat16"
+
+    # Optional: use MLP on L instead of attention (matches HRM/TRM option)
+    mlp_t: bool = False
+
+    # ---- GLPS extras (tiny) ----
+    glps_enabled: bool = True
+    glps_fill_obvious: bool = True
+    glps_dep_graph: bool = True
+    glps_token_masking: bool = True
+    glps_global_propagate_on_low_conf: bool = True
+
+    glps_tau_halt: float = 0.95       # final confidence to halt
+    glps_tau_uncertain: float = 0.60  # cell-wise certainty threshold
+    glps_max_targeted_iters: int = 2  # small number: 1-2
+
+    # Dependency scorer (low rank bilinear)
+    dep_rank: int = 32
+    dep_topk: int = 8
+
+class GLPSBlock(nn.Module):
+    def __init__(self, config: GLPS_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        if self.config.mlp_t:
+            self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size)
+            self.mlp_t = SwiGLU(
+                hidden_size=self.config.seq_len + self.puzzle_emb_len, # treat sequence as channel
+                expansion=config.expansion,
+            )
+        else:
+            self.self_attn = Attention(
+                hidden_size=config.hidden_size,
+                head_dim=config.hidden_size // config.num_heads,
+                num_heads=config.num_heads,
+                num_key_value_heads=config.num_heads,
+                causal=False,
+            )
+        self.mlp = SwiGLU(hidden_size=config.hidden_size, expansion=config.expansion)
+        self.norm_eps = config.rms_norm_eps
+
+    def forward(self, cos_sin: CosSin, hidden_states: torch.Tensor) -> torch.Tensor:
+        if self.config.mlp_t:
+            # MLP over sequence dimension (mlp-t)
+            hidden_states = hidden_states.transpose(1, 2)
+            out = self.mlp_t(hidden_states)
+            hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+            hidden_states = hidden_states.transpose(1, 2)
+        else:
+            hidden_states = rms_norm(
+                hidden_states + self.self_attn(cos_sin=cos_sin, hidden_states=hidden_states),
+                variance_epsilon=self.norm_eps,
+            )
+        out = self.mlp(hidden_states)
+        hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+        return hidden_states
+
+class GLPSReasoningModule(nn.Module):
+    """Reasoning stack with optional masked updates (only update uncertain tokens)."""
+    def __init__(self, layers: List[GLPSBlock]):
+        super().__init__()
+        self.layers = torch.nn.ModuleList(layers)
+
+    def forward(self, hidden_states: torch.Tensor, input_injection: torch.Tensor, update_mask: torch.Tensor = None, **kwargs) -> torch.Tensor:
+        x = hidden_states
+        for layer in self.layers:
+            # Compute candidate update using injected context
+            y = layer(hidden_states=x + input_injection, **kwargs)
+            if update_mask is not None:
+                # Convex blend keeps frozen tokens unchanged
+                m = update_mask.to(x.dtype)[..., None]
+                x = x + m * (y - x)
+            else:
+                x = y
+        return x
+
+class GLPS_ACTV1_Inner(nn.Module):
+    def __init__(self, config: GLPS_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        self.forward_dtype = getattr(torch, self.config.forward_dtype)
+
+        # I/O
+        self.embed_scale  = math.sqrt(self.config.hidden_size)
+        embed_init_std = 1.0 / self.embed_scale
+
+        self.embed_tokens = CastedEmbedding(self.config.vocab_size, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+        self.lm_head      = CastedLinear(self.config.hidden_size, self.config.vocab_size, bias=False)
+        self.q_head       = CastedLinear(self.config.hidden_size, 2, bias=True)
+
+        # Puzzle emb (optional) — same convention as HRM/TRM
+        self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size)  # ceil div
+        if self.config.puzzle_emb_ndim > 0:
+            self.puzzle_emb = CastedSparseEmbedding(self.config.num_puzzle_identifiers, self.config.puzzle_emb_ndim, batch_size=self.config.batch_size, init_std=0, cast_to=self.forward_dtype)
+
+        # Positional encodings
+        if self.config.pos_encodings == "rope":
+            self.rotary_emb = RotaryEmbedding(dim=self.config.hidden_size // self.config.num_heads, max_position_embeddings=self.config.seq_len + self.puzzle_emb_len, base=self.config.rope_theta)
+        elif self.config.pos_encodings == "learned":
+            self.embed_pos = CastedEmbedding(self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+
+        # Reasoning stacks
+        self.H_level = GLPSReasoningModule(layers=[GLPSBlock(self.config) for _ in range(self.config.H_layers)])
+        self.L_level = GLPSReasoningModule(layers=[GLPSBlock(self.config) for _ in range(self.config.L_layers)])
+
+        # Initial states (match HRM/TRM style)
+        H_init = trunc_normal_init_(torch.empty(1, 1, self.config.hidden_size, dtype=self.forward_dtype), std=1.0)
+        L_init = trunc_normal_init_(torch.empty(1, 1, self.config.hidden_size, dtype=self.forward_dtype), std=1.0)
+        self.register_buffer("H_init", H_init, persistent=True)
+        self.register_buffer("L_init", L_init, persistent=True)
+
+        # GLPS small heads
+        self.candidate_head = CastedLinear(self.config.hidden_size, 16, bias=True)  # task-specific; for Sudoku you can slice to 9
+        self.certainty_head = CastedLinear(self.config.hidden_size, 1, bias=True)
+        self.energy_head    = CastedLinear(self.config.hidden_size, 1, bias=True)
+
+        # Low-rank dependency scorer (shared)
+        r = max(1, self.config.dep_rank)
+        self.dep_q = CastedLinear(self.config.hidden_size, r, bias=False)
+        self.dep_k = CastedLinear(self.config.hidden_size, r, bias=False)
+
+        # Q head init like HRM/TRM (near-zero -> easier bootstrapping)
+        with torch.no_grad():
+            self.q_head.weight.zero_()
+            self.q_head.bias.fill_(-5)
+
+    def _input_embeddings(self, input: torch.Tensor, puzzle_identifiers: torch.Tensor):
+        # Token embedding
+        embedding = self.embed_tokens(input.to(torch.int32))
+
+        # Puzzle embeddings
+        if self.config.puzzle_emb_ndim > 0:
+            puzzle_embedding = self.puzzle_emb(puzzle_identifiers)
+            pad_count = self.puzzle_emb_len * self.config.hidden_size - puzzle_embedding.shape[-1]
+            if pad_count > 0:
+                puzzle_embedding = F.pad(puzzle_embedding, (0, pad_count))
+            embedding = torch.cat((puzzle_embedding.view(-1, self.puzzle_emb_len, self.config.hidden_size), embedding), dim=-2)
+
+        # Position embeddings
+        if self.config.pos_encodings == "learned":
+            embedding = 0.707106781 * (embedding + self.embed_pos.embedding_weight.to(self.forward_dtype))
+
+        return self.embed_scale * embedding
+
+    def empty_carry(self, batch_size: int):
+        return GLPS_ACTV1InnerCarry(
+            z_H=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+            z_L=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+        )
+
+    def reset_carry(self, reset_flag: torch.Tensor, carry: GLPS_ACTV1InnerCarry):
+        # Explicitly expand buffers and mask to target shapes to avoid shape confusion
+        B, L, D = carry.z_H.shape
+        # Reduce/reset flag to per-batch boolean vector of shape [B]
+        if reset_flag.ndim == 1 and reset_flag.shape[0] == B:
+            reset_b = reset_flag.to(torch.bool)
+        else:
+            # If shape is [B, ...] reduce across non-batch dims; otherwise fallback to first B entries
+            try:
+                reset_b = reset_flag.reshape(B, -1).any(dim=1).to(torch.bool)
+            except Exception:
+                reset_b = reset_flag.reshape(-1)[:B].to(torch.bool)
+        m = reset_b.view(B, 1, 1)
+        mH = m.expand(B, L, D)
+        mL = mH  # same shape for z_L
+        H_init_exp = self.H_init.expand(B, L, D)
+        L_init_exp = self.L_init.expand(B, L, D)
+        return GLPS_ACTV1InnerCarry(
+            z_H=torch.where(mH, H_init_exp, carry.z_H),
+            z_L=torch.where(mL, L_init_exp, carry.z_L),
+        )
+
+    def _global_scan(self, z_L, z_H, input_embeddings, seq_info):
+        # One light pass to gather global signals
+        z_scan = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+        cand_logits = self.candidate_head(z_scan)              # [B, L, C]
+        certainty   = torch.sigmoid(self.certainty_head(z_scan))  # [B, L, 1]
+        return z_scan, cand_logits, certainty
+
+    def _build_dep_mask(self, z_ctx, uncertain_mask: torch.Tensor):
+        """Compute a dependency-based focus mask from a low-rank bilinear score.
+        uncertain_mask: [B, L] boolean mask of cells that are currently uncertain
+        Returns: dep_mask [B, L] boolean mask of cells to (re)update.
+        """
+        B, L, D = z_ctx.shape
+        # Project to low-rank space; use float32 to avoid dtype mismatches under torch.compile
+        Q = self.dep_q(z_ctx).to(torch.float32)   # [B, L, r]
+        K = self.dep_k(z_ctx).to(torch.float32)   # [B, L, r]
+        r = max(1, int(Q.shape[-1]))
+        sim = torch.matmul(Q, K.transpose(1, 2))  # [B, L, L] (float32)
+        sim = sim / math.sqrt(r)
+
+        # Aggregate influence from uncertain queries onto target tokens
+        src = uncertain_mask.to(sim.dtype).unsqueeze(1)      # [B, 1, L]
+        influence = torch.matmul(src, sim).squeeze(1)        # [B, L]
+
+        # Top-k influenced tokens per batch
+        topk = min(self.config.dep_topk, L)
+        vals, idx = torch.topk(influence, k=topk, dim=-1)    # [B, topk]
+        dep_mask = torch.zeros_like(uncertain_mask)
+        dep_mask.scatter_(1, idx, True)
+
+        # Always include uncertain cells themselves
+        dep_mask = dep_mask | uncertain_mask
+        return dep_mask
+
+    def forward(self, carry: GLPS_ACTV1InnerCarry, batch: Dict[str, torch.Tensor]):
+        seq_info = dict(cos_sin=getattr(self, "rotary_emb", None)() if hasattr(self, "rotary_emb") else None)
+
+        # Encode inputs
+        input_embeddings = self._input_embeddings(batch["inputs"], batch["puzzle_identifiers"])
+
+        # States
+        z_H, z_L = carry.z_H, carry.z_L
+
+        if not self.config.glps_enabled:
+            # Fallback to an HRM-like single-cycle grad update for compatibility
+            with torch.no_grad():
+                for _H in range(self.config.H_cycles - 1):
+                    for _L in range(self.config.L_cycles):
+                        z_L = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+                    z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+            # final grad step
+            for _L in range(self.config.L_cycles):
+                z_L = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+            z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+
+            # Outputs
+            new_carry = GLPS_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach())
+            logits = self.lm_head(z_H)[:, self.puzzle_emb_len:]
+            q_logits = self.q_head(z_H[:, 0]).to(torch.float32)
+            conf = torch.zeros_like(q_logits[..., :1]) + 0.5  # neutral
+            return new_carry, logits, (q_logits[..., 0], q_logits[..., 1]), conf
+
+        # ===== GLPS path =====
+        # H1: global scan (cheap)
+        with torch.no_grad():
+            z_scan, cand_logits, certainty = self._global_scan(z_L, z_H, input_embeddings, seq_info)
+
+        # L1: fill-obvious -> compute stable vs uncertain masks
+        if self.config.glps_fill_obvious:
+            obvious_mask = (certainty >= self.config.glps_tau_uncertain)  # [B, L, 1]
+        else:
+            obvious_mask = torch.zeros_like(certainty).bool()
+        stable_mask = obvious_mask.squeeze(-1)         # [B, L]
+        uncertain_mask = ~stable_mask                  # [B, L]
+
+        # H2: dependency prediction over remaining cells
+        if self.config.glps_dep_graph:
+            dep_mask = self._build_dep_mask(z_scan, uncertain_mask)   # [B, L]
+        else:
+            dep_mask = uncertain_mask
+
+        # L2: targeted refinement (a couple of masked iters)
+        update_mask = dep_mask if self.config.glps_token_masking else None
+        z = z_scan.detach()  # use scanned context as start
+        for _ in range(self.config.glps_max_targeted_iters):
+            z = self.L_level(z, z_H + input_embeddings, update_mask=update_mask, **seq_info)
+            # Refresh certainty to shrink mask (optional but cheap)
+            cert_now = torch.sigmoid(self.certainty_head(z)).squeeze(-1)
+            if self.config.glps_token_masking:
+                update_mask = dep_mask & (cert_now < self.config.glps_tau_uncertain)
+
+        # Merge into H and do a light H update with grad
+        z_L = z
+        z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+
+        # H3: energy/consistency -> confidence & optional global propagate
+        energy = torch.sigmoid(self.energy_head(z_H)).mean(dim=1)  # [B, 1]
+        conf = 1.0 - energy
+        need_sweep = (conf.squeeze(-1) < self.config.glps_tau_halt)
+        if self.config.glps_global_propagate_on_low_conf and need_sweep.any():
+            # one final full sweep only for rows needing it
+            maskB = need_sweep.view(-1, 1, 1)
+            zL2 = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+            zH2 = self.H_level(z_H, zL2, update_mask=None, **seq_info)
+            z_L = torch.where(maskB, zL2, z_L)
+            z_H = torch.where(maskB, zH2, z_H)
+
+        # Outputs
+        new_carry = GLPS_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach())
+        logits = self.lm_head(z_H)[:, self.puzzle_emb_len:]
+        q_logits = self.q_head(z_H[:, 0]).to(torch.float32)
+        return new_carry, logits, (q_logits[..., 0], q_logits[..., 1]), conf
+
+class GLPS_ACTV1(nn.Module):
+    """ACT-style wrapper that mixes Q-halt with GLPS confidence."""
+    def __init__(self, config_dict: dict):
+        super().__init__()
+        self.config = GLPS_ACTV1Config(**config_dict)
+        self.inner = GLPS_ACTV1_Inner(self.config)
+
+    @property
+    def puzzle_emb(self):
+        return self.inner.puzzle_emb
+
+    def initial_carry(self, batch: Dict[str, torch.Tensor]):
+        batch_size = batch["inputs"].shape[0]
+        return GLPS_ACTV1Carry(
+            inner_carry=self.inner.empty_carry(batch_size),
+            steps=torch.zeros((batch_size,), dtype=torch.int32),
+            halted=torch.ones((batch_size,), dtype=torch.bool),  # start halted to force reset on first pass
+            current_data={k: torch.empty_like(v) for k, v in batch.items()}
+        )
+
+    def forward(self, carry: GLPS_ACTV1Carry, batch: Dict[str, torch.Tensor]):
+        # Reset halted seqs
+        new_inner_carry = self.inner.reset_carry(carry.halted, carry.inner_carry)
+        new_steps = torch.where(carry.halted, 0, carry.steps)
+        new_current_data = {k: torch.where(carry.halted.view((-1,) + (1,) * (batch[k].ndim - 1)), batch[k], v) for k, v in carry.current_data.items()}
+
+        # Inner step
+        new_inner_carry, logits, (q_halt_logits, q_continue_logits), conf = self.inner(new_inner_carry, new_current_data)
+
+        outputs = {
+            "logits": logits,
+            "q_halt_logits": q_halt_logits,
+            "q_continue_logits": q_continue_logits,
+            "conf": conf.squeeze(-1),
+        }
+
+        with torch.no_grad():
+            new_steps = new_steps + 1
+            is_last_step = new_steps >= self.config.halt_max_steps
+
+            # Combine halt signals: Q or confidence or last-step
+            halted = is_last_step | (q_halt_logits > q_continue_logits) | (conf.squeeze(-1) >= self.config.glps_tau_halt)
+
+            # Exploration during training only
+            if self.training and (self.config.halt_max_steps > 1):
+                min_halt_steps = (torch.rand_like(q_halt_logits) < self.config.halt_exploration_prob) * torch.randint_like(new_steps, low=2, high=self.config.halt_max_steps + 1)
+                halted = halted & (new_steps >= min_halt_steps)
+                
+                # Optional target for Q-learning (kept similar to HRM)
+                next_conf = self.inner(new_inner_carry, new_current_data)[-1]
+                outputs["target_q_continue"] = torch.sigmoid(torch.where(is_last_step, q_halt_logits, torch.maximum(q_halt_logits, q_continue_logits)))
+            else:
+                # During eval, always use max_steps to ensure consistent reasoning depth (same as TRM/HRM eval behavior)
+                halted = is_last_step
+
+        return GLPS_ACTV1Carry(new_inner_carry, new_steps, halted, new_current_data), outputs
diff --git a/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_e10k_b160/losses.py b/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_e10k_b160/losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..cdfb77718bc16f98cdd7da06eb5795c36b866aa7
--- /dev/null
+++ b/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_e10k_b160/losses.py
@@ -0,0 +1,102 @@
+from typing import Any, Tuple, Dict, Sequence, Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+import math
+
+IGNORE_LABEL_ID = -100
+
+
+def s(x, epsilon=1e-30):
+    return torch.where(
+        x<0,
+        1/(1-x+ epsilon),
+        x + 1
+    )
+
+
+def log_stablemax(x, dim=-1):
+    s_x = s(x)
+    return torch.log(s_x/torch.sum(s_x, dim=dim, keepdim=True))
+
+
+def stablemax_cross_entropy(logits, labels, ignore_index: int = -100, valid_mask=None):
+    logprobs = log_stablemax(logits.to(torch.float64), dim=-1)
+
+    if valid_mask is None:
+        valid_mask = (labels != ignore_index)
+    transformed_labels = torch.where(valid_mask, labels, 0)
+    prediction_logprobs = torch.gather(logprobs, index=transformed_labels.to(torch.long).unsqueeze(-1), dim=-1).squeeze(-1)
+
+    return -torch.where(valid_mask, prediction_logprobs, 0)
+
+
+def softmax_cross_entropy(logits, labels, ignore_index: int = -100):
+    # Cast logits to f32
+    # Flatten logits
+    return F.cross_entropy(logits.to(torch.float32).view(-1, logits.shape[-1]), labels.to(torch.long).view(-1), ignore_index=ignore_index, reduction="none").view(labels.shape)
+
+
+class ACTLossHead(nn.Module):
+    def __init__(self, model: nn.Module, loss_type: str):
+        super().__init__()
+        self.model = model
+        self.loss_fn = globals()[loss_type]
+        
+    def initial_carry(self, *args, **kwargs):
+        return self.model.initial_carry(*args, **kwargs)  # type: ignore
+
+    def forward(
+        self,
+        return_keys: Sequence[str],
+        # Model args
+        **model_kwargs,
+    ) -> Tuple[Any, torch.Tensor, Dict[str, torch.Tensor], Optional[Dict[str, torch.Tensor]], torch.Tensor]:
+        # Model logits
+        # B x SeqLen x D
+        new_carry, outputs = self.model(**model_kwargs)
+        labels = new_carry.current_data["labels"]
+
+        with torch.no_grad():
+            # Preds
+            outputs["preds"] = torch.argmax(outputs["logits"], dim=-1)
+
+            # Correctness
+            mask = (labels != IGNORE_LABEL_ID)
+            loss_counts = mask.sum(-1)
+            loss_divisor = loss_counts.clamp_min(1).unsqueeze(-1)  # Avoid NaNs in division
+
+            is_correct = mask & (torch.argmax(outputs["logits"], dim=-1) == labels)
+            seq_is_correct = is_correct.sum(-1) == loss_counts
+            
+            # Metrics (halted)
+            valid_metrics = new_carry.halted & (loss_counts > 0)
+            metrics = {
+                "count": valid_metrics.sum(),
+                
+                "accuracy":       torch.where(valid_metrics, (is_correct.to(torch.float32) / loss_divisor).sum(-1), 0).sum(),
+                "exact_accuracy": (valid_metrics & seq_is_correct).sum(),
+
+                "q_halt_accuracy": (valid_metrics & ((outputs["q_halt_logits"] >= 0) == seq_is_correct)).sum(),
+                "steps":          torch.where(valid_metrics, new_carry.steps, 0).sum(),
+            }
+
+        # Losses
+
+        lm_loss = (self.loss_fn(outputs["logits"], labels, ignore_index=IGNORE_LABEL_ID, valid_mask=mask) / loss_divisor).sum()
+        q_halt_loss = F.binary_cross_entropy_with_logits(outputs["q_halt_logits"], seq_is_correct.to(outputs["q_halt_logits"].dtype), reduction="sum")
+        metrics.update({
+            "lm_loss": lm_loss.detach(),
+            "q_halt_loss": q_halt_loss.detach(),
+        })
+        # Q continue (bootstrapping target loss); Alexia: This fits Q-learning, but seems totally unecessary
+        q_continue_loss = 0
+        if "target_q_continue" in outputs:
+            q_continue_loss = F.binary_cross_entropy_with_logits(outputs["q_continue_logits"], outputs["target_q_continue"], reduction="sum")
+
+            metrics["q_continue_loss"] = q_continue_loss.detach()
+        # Filter outputs for return
+        detached_outputs = {k: outputs[k].detach() for k in return_keys if k in outputs}
+
+        return new_carry, lm_loss + 0.5 * (q_halt_loss + q_continue_loss), metrics, detached_outputs, new_carry.halted.all()
diff --git a/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_e10k_evalboost_extreme/all_config.yaml b/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_e10k_evalboost_extreme/all_config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..7ad9892dc49f53dccc2bd5b1694af3ac84e7a6ba
--- /dev/null
+++ b/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_e10k_evalboost_extreme/all_config.yaml
@@ -0,0 +1,60 @@
+arch:
+  H_cycles: 3
+  H_layers: 2
+  L_cycles: 1
+  L_layers: 7
+  dep_rank: 64
+  dep_topk: 12
+  expansion: 4.0
+  forward_dtype: bfloat16
+  glps_dep_graph: true
+  glps_enabled: true
+  glps_fill_obvious: true
+  glps_global_propagate_on_low_conf: true
+  glps_max_targeted_iters: 4
+  glps_tau_halt: 0.95
+  glps_tau_uncertain: 0.8
+  glps_token_masking: true
+  halt_exploration_prob: 0.1
+  halt_max_steps: 16
+  hidden_size: 512
+  loss:
+    loss_type: stablemax_cross_entropy
+    name: losses@ACTLossHead
+  mlp_t: false
+  name: recursive_reasoning.glps@GLPS_ACTV1
+  num_heads: 8
+  pos_encodings: rope
+  puzzle_emb_ndim: 0
+  rms_norm_eps: 1.0e-05
+  rope_theta: 10000.0
+beta1: 0.9
+beta2: 0.95
+checkpoint_every_eval: true
+checkpoint_path: checkpoints/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_e10k_evalboost_extreme
+data_paths:
+- data/maze-30x30-hard-1k
+data_paths_test: []
+ema: true
+ema_rate: 0.999
+epochs: 10000
+eval_glps_max_targeted_iters: 10
+eval_glps_tau_halt: 0.75
+eval_halt_max_steps: 48
+eval_interval: 1000
+eval_only: true
+eval_save_outputs: []
+evaluators: []
+freeze_weights: false
+global_batch_size: 160
+load_checkpoint: checkpoints/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_e10k_b160/step_6250
+lr: 0.0001
+lr_min_ratio: 0.1
+lr_warmup_steps: 2000
+min_eval_interval: 0
+project_name: Maze-30x30-hard-1k-ACT-torch
+puzzle_emb_lr: 0.0001
+puzzle_emb_weight_decay: 1.0
+run_name: pretrain_glps_maze30_v1_e10k_evalboost_extreme
+seed: 0
+weight_decay: 1.0
diff --git a/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_e10k_evalboost_extreme/glps.py b/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_e10k_evalboost_extreme/glps.py
new file mode 100644
index 0000000000000000000000000000000000000000..96e6de932e79e174cf773779293a643842e8a268
--- /dev/null
+++ b/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_e10k_evalboost_extreme/glps.py
@@ -0,0 +1,409 @@
+
+from typing import Tuple, List, Dict
+from dataclasses import dataclass
+import math
+import torch
+import torch.nn.functional as F
+from torch import nn
+from pydantic import BaseModel
+
+# Reuse the same building blocks as HRM/TRM
+from models.common import trunc_normal_init_
+from models.layers import rms_norm, SwiGLU, Attention, RotaryEmbedding, CosSin, CastedEmbedding, CastedLinear
+from models.sparse_embedding import CastedSparseEmbedding
+
+"""
+Global-Local Predictive Solver (GLPS)
+------------------------------------
+A light-weight control-policy on top of the HRM/TRM style blocks:
+- H1: global scan -> certainty map
+- L1: fill-obvious (lock stable cells)
+- H2: dependency scoring over remaining cells
+- L2: targeted refinement (masked updates)
+- H3: energy-based confidence -> (optional) one global propagate sweep -> halt
+
+This file keeps parameter growth tiny: a few heads + (optional) low-rank dependency scorer.
+"""
+
+@dataclass
+class GLPS_ACTV1InnerCarry:
+    z_H: torch.Tensor
+    z_L: torch.Tensor
+
+@dataclass
+class GLPS_ACTV1Carry:
+    inner_carry: GLPS_ACTV1InnerCarry
+    steps: torch.Tensor
+    halted: torch.Tensor
+    current_data: Dict[str, torch.Tensor]
+
+class GLPS_ACTV1Config(BaseModel):
+    # Core IO / shapes
+    batch_size: int
+    seq_len: int
+    puzzle_emb_ndim: int = 0
+    num_puzzle_identifiers: int = 1
+    vocab_size: int = 256
+
+    # Cycle schedule
+    H_cycles: int = 3              # (scan -> refine -> check) typical
+    L_cycles: int = 1
+
+    # Depth
+    H_layers: int = 2
+    L_layers: int = 4
+
+    # Transformer config
+    hidden_size: int = 512
+    expansion: float = 2.0
+    num_heads: int = 8
+    pos_encodings: str = "rope"
+
+    rms_norm_eps: float = 1e-5
+    rope_theta: float = 10000.0
+
+    # ACT wrapper
+    halt_max_steps: int = 4
+    halt_exploration_prob: float = 0.1
+
+    forward_dtype: str = "bfloat16"
+
+    # Optional: use MLP on L instead of attention (matches HRM/TRM option)
+    mlp_t: bool = False
+
+    # ---- GLPS extras (tiny) ----
+    glps_enabled: bool = True
+    glps_fill_obvious: bool = True
+    glps_dep_graph: bool = True
+    glps_token_masking: bool = True
+    glps_global_propagate_on_low_conf: bool = True
+
+    glps_tau_halt: float = 0.95       # final confidence to halt
+    glps_tau_uncertain: float = 0.60  # cell-wise certainty threshold
+    glps_max_targeted_iters: int = 2  # small number: 1-2
+
+    # Dependency scorer (low rank bilinear)
+    dep_rank: int = 32
+    dep_topk: int = 8
+
+class GLPSBlock(nn.Module):
+    def __init__(self, config: GLPS_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        if self.config.mlp_t:
+            self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size)
+            self.mlp_t = SwiGLU(
+                hidden_size=self.config.seq_len + self.puzzle_emb_len, # treat sequence as channel
+                expansion=config.expansion,
+            )
+        else:
+            self.self_attn = Attention(
+                hidden_size=config.hidden_size,
+                head_dim=config.hidden_size // config.num_heads,
+                num_heads=config.num_heads,
+                num_key_value_heads=config.num_heads,
+                causal=False,
+            )
+        self.mlp = SwiGLU(hidden_size=config.hidden_size, expansion=config.expansion)
+        self.norm_eps = config.rms_norm_eps
+
+    def forward(self, cos_sin: CosSin, hidden_states: torch.Tensor) -> torch.Tensor:
+        if self.config.mlp_t:
+            # MLP over sequence dimension (mlp-t)
+            hidden_states = hidden_states.transpose(1, 2)
+            out = self.mlp_t(hidden_states)
+            hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+            hidden_states = hidden_states.transpose(1, 2)
+        else:
+            hidden_states = rms_norm(
+                hidden_states + self.self_attn(cos_sin=cos_sin, hidden_states=hidden_states),
+                variance_epsilon=self.norm_eps,
+            )
+        out = self.mlp(hidden_states)
+        hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+        return hidden_states
+
+class GLPSReasoningModule(nn.Module):
+    """Reasoning stack with optional masked updates (only update uncertain tokens)."""
+    def __init__(self, layers: List[GLPSBlock]):
+        super().__init__()
+        self.layers = torch.nn.ModuleList(layers)
+
+    def forward(self, hidden_states: torch.Tensor, input_injection: torch.Tensor, update_mask: torch.Tensor = None, **kwargs) -> torch.Tensor:
+        x = hidden_states
+        for layer in self.layers:
+            # Compute candidate update using injected context
+            y = layer(hidden_states=x + input_injection, **kwargs)
+            if update_mask is not None:
+                # Convex blend keeps frozen tokens unchanged
+                m = update_mask.to(x.dtype)[..., None]
+                x = x + m * (y - x)
+            else:
+                x = y
+        return x
+
+class GLPS_ACTV1_Inner(nn.Module):
+    def __init__(self, config: GLPS_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        self.forward_dtype = getattr(torch, self.config.forward_dtype)
+
+        # I/O
+        self.embed_scale  = math.sqrt(self.config.hidden_size)
+        embed_init_std = 1.0 / self.embed_scale
+
+        self.embed_tokens = CastedEmbedding(self.config.vocab_size, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+        self.lm_head      = CastedLinear(self.config.hidden_size, self.config.vocab_size, bias=False)
+        self.q_head       = CastedLinear(self.config.hidden_size, 2, bias=True)
+
+        # Puzzle emb (optional) — same convention as HRM/TRM
+        self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size)  # ceil div
+        if self.config.puzzle_emb_ndim > 0:
+            self.puzzle_emb = CastedSparseEmbedding(self.config.num_puzzle_identifiers, self.config.puzzle_emb_ndim, batch_size=self.config.batch_size, init_std=0, cast_to=self.forward_dtype)
+
+        # Positional encodings
+        if self.config.pos_encodings == "rope":
+            self.rotary_emb = RotaryEmbedding(dim=self.config.hidden_size // self.config.num_heads, max_position_embeddings=self.config.seq_len + self.puzzle_emb_len, base=self.config.rope_theta)
+        elif self.config.pos_encodings == "learned":
+            self.embed_pos = CastedEmbedding(self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+
+        # Reasoning stacks
+        self.H_level = GLPSReasoningModule(layers=[GLPSBlock(self.config) for _ in range(self.config.H_layers)])
+        self.L_level = GLPSReasoningModule(layers=[GLPSBlock(self.config) for _ in range(self.config.L_layers)])
+
+        # Initial states (match HRM/TRM style)
+        H_init = trunc_normal_init_(torch.empty(1, 1, self.config.hidden_size, dtype=self.forward_dtype), std=1.0)
+        L_init = trunc_normal_init_(torch.empty(1, 1, self.config.hidden_size, dtype=self.forward_dtype), std=1.0)
+        self.register_buffer("H_init", H_init, persistent=True)
+        self.register_buffer("L_init", L_init, persistent=True)
+
+        # GLPS small heads
+        self.candidate_head = CastedLinear(self.config.hidden_size, 16, bias=True)  # task-specific; for Sudoku you can slice to 9
+        self.certainty_head = CastedLinear(self.config.hidden_size, 1, bias=True)
+        self.energy_head    = CastedLinear(self.config.hidden_size, 1, bias=True)
+
+        # Low-rank dependency scorer (shared)
+        r = max(1, self.config.dep_rank)
+        self.dep_q = CastedLinear(self.config.hidden_size, r, bias=False)
+        self.dep_k = CastedLinear(self.config.hidden_size, r, bias=False)
+
+        # Q head init like HRM/TRM (near-zero -> easier bootstrapping)
+        with torch.no_grad():
+            self.q_head.weight.zero_()
+            self.q_head.bias.fill_(-5)
+
+    def _input_embeddings(self, input: torch.Tensor, puzzle_identifiers: torch.Tensor):
+        # Token embedding
+        embedding = self.embed_tokens(input.to(torch.int32))
+
+        # Puzzle embeddings
+        if self.config.puzzle_emb_ndim > 0:
+            puzzle_embedding = self.puzzle_emb(puzzle_identifiers)
+            pad_count = self.puzzle_emb_len * self.config.hidden_size - puzzle_embedding.shape[-1]
+            if pad_count > 0:
+                puzzle_embedding = F.pad(puzzle_embedding, (0, pad_count))
+            embedding = torch.cat((puzzle_embedding.view(-1, self.puzzle_emb_len, self.config.hidden_size), embedding), dim=-2)
+
+        # Position embeddings
+        if self.config.pos_encodings == "learned":
+            embedding = 0.707106781 * (embedding + self.embed_pos.embedding_weight.to(self.forward_dtype))
+
+        return self.embed_scale * embedding
+
+    def empty_carry(self, batch_size: int):
+        return GLPS_ACTV1InnerCarry(
+            z_H=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+            z_L=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+        )
+
+    def reset_carry(self, reset_flag: torch.Tensor, carry: GLPS_ACTV1InnerCarry):
+        # Explicitly expand buffers and mask to target shapes to avoid shape confusion
+        B, L, D = carry.z_H.shape
+        # Reduce/reset flag to per-batch boolean vector of shape [B]
+        if reset_flag.ndim == 1 and reset_flag.shape[0] == B:
+            reset_b = reset_flag.to(torch.bool)
+        else:
+            # If shape is [B, ...] reduce across non-batch dims; otherwise fallback to first B entries
+            try:
+                reset_b = reset_flag.reshape(B, -1).any(dim=1).to(torch.bool)
+            except Exception:
+                reset_b = reset_flag.reshape(-1)[:B].to(torch.bool)
+        m = reset_b.view(B, 1, 1)
+        mH = m.expand(B, L, D)
+        mL = mH  # same shape for z_L
+        H_init_exp = self.H_init.expand(B, L, D)
+        L_init_exp = self.L_init.expand(B, L, D)
+        return GLPS_ACTV1InnerCarry(
+            z_H=torch.where(mH, H_init_exp, carry.z_H),
+            z_L=torch.where(mL, L_init_exp, carry.z_L),
+        )
+
+    def _global_scan(self, z_L, z_H, input_embeddings, seq_info):
+        # One light pass to gather global signals
+        z_scan = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+        cand_logits = self.candidate_head(z_scan)              # [B, L, C]
+        certainty   = torch.sigmoid(self.certainty_head(z_scan))  # [B, L, 1]
+        return z_scan, cand_logits, certainty
+
+    def _build_dep_mask(self, z_ctx, uncertain_mask: torch.Tensor):
+        """Compute a dependency-based focus mask from a low-rank bilinear score.
+        uncertain_mask: [B, L] boolean mask of cells that are currently uncertain
+        Returns: dep_mask [B, L] boolean mask of cells to (re)update.
+        """
+        B, L, D = z_ctx.shape
+        # Project to low-rank space; use float32 to avoid dtype mismatches under torch.compile
+        Q = self.dep_q(z_ctx).to(torch.float32)   # [B, L, r]
+        K = self.dep_k(z_ctx).to(torch.float32)   # [B, L, r]
+        r = max(1, int(Q.shape[-1]))
+        sim = torch.matmul(Q, K.transpose(1, 2))  # [B, L, L] (float32)
+        sim = sim / math.sqrt(r)
+
+        # Aggregate influence from uncertain queries onto target tokens
+        src = uncertain_mask.to(sim.dtype).unsqueeze(1)      # [B, 1, L]
+        influence = torch.matmul(src, sim).squeeze(1)        # [B, L]
+
+        # Top-k influenced tokens per batch
+        topk = min(self.config.dep_topk, L)
+        vals, idx = torch.topk(influence, k=topk, dim=-1)    # [B, topk]
+        dep_mask = torch.zeros_like(uncertain_mask)
+        dep_mask.scatter_(1, idx, True)
+
+        # Always include uncertain cells themselves
+        dep_mask = dep_mask | uncertain_mask
+        return dep_mask
+
+    def forward(self, carry: GLPS_ACTV1InnerCarry, batch: Dict[str, torch.Tensor]):
+        seq_info = dict(cos_sin=getattr(self, "rotary_emb", None)() if hasattr(self, "rotary_emb") else None)
+
+        # Encode inputs
+        input_embeddings = self._input_embeddings(batch["inputs"], batch["puzzle_identifiers"])
+
+        # States
+        z_H, z_L = carry.z_H, carry.z_L
+
+        if not self.config.glps_enabled:
+            # Fallback to an HRM-like single-cycle grad update for compatibility
+            with torch.no_grad():
+                for _H in range(self.config.H_cycles - 1):
+                    for _L in range(self.config.L_cycles):
+                        z_L = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+                    z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+            # final grad step
+            for _L in range(self.config.L_cycles):
+                z_L = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+            z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+
+            # Outputs
+            new_carry = GLPS_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach())
+            logits = self.lm_head(z_H)[:, self.puzzle_emb_len:]
+            q_logits = self.q_head(z_H[:, 0]).to(torch.float32)
+            conf = torch.zeros_like(q_logits[..., :1]) + 0.5  # neutral
+            return new_carry, logits, (q_logits[..., 0], q_logits[..., 1]), conf
+
+        # ===== GLPS path =====
+        # H1: global scan (cheap)
+        with torch.no_grad():
+            z_scan, cand_logits, certainty = self._global_scan(z_L, z_H, input_embeddings, seq_info)
+
+        # L1: fill-obvious -> compute stable vs uncertain masks
+        if self.config.glps_fill_obvious:
+            obvious_mask = (certainty >= self.config.glps_tau_uncertain)  # [B, L, 1]
+        else:
+            obvious_mask = torch.zeros_like(certainty).bool()
+        stable_mask = obvious_mask.squeeze(-1)         # [B, L]
+        uncertain_mask = ~stable_mask                  # [B, L]
+
+        # H2: dependency prediction over remaining cells
+        if self.config.glps_dep_graph:
+            dep_mask = self._build_dep_mask(z_scan, uncertain_mask)   # [B, L]
+        else:
+            dep_mask = uncertain_mask
+
+        # L2: targeted refinement (a couple of masked iters)
+        update_mask = dep_mask if self.config.glps_token_masking else None
+        z = z_scan.detach()  # use scanned context as start
+        for _ in range(self.config.glps_max_targeted_iters):
+            z = self.L_level(z, z_H + input_embeddings, update_mask=update_mask, **seq_info)
+            # Refresh certainty to shrink mask (optional but cheap)
+            cert_now = torch.sigmoid(self.certainty_head(z)).squeeze(-1)
+            if self.config.glps_token_masking:
+                update_mask = dep_mask & (cert_now < self.config.glps_tau_uncertain)
+
+        # Merge into H and do a light H update with grad
+        z_L = z
+        z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+
+        # H3: energy/consistency -> confidence & optional global propagate
+        energy = torch.sigmoid(self.energy_head(z_H)).mean(dim=1)  # [B, 1]
+        conf = 1.0 - energy
+        need_sweep = (conf.squeeze(-1) < self.config.glps_tau_halt)
+        if self.config.glps_global_propagate_on_low_conf and need_sweep.any():
+            # one final full sweep only for rows needing it
+            maskB = need_sweep.view(-1, 1, 1)
+            zL2 = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+            zH2 = self.H_level(z_H, zL2, update_mask=None, **seq_info)
+            z_L = torch.where(maskB, zL2, z_L)
+            z_H = torch.where(maskB, zH2, z_H)
+
+        # Outputs
+        new_carry = GLPS_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach())
+        logits = self.lm_head(z_H)[:, self.puzzle_emb_len:]
+        q_logits = self.q_head(z_H[:, 0]).to(torch.float32)
+        return new_carry, logits, (q_logits[..., 0], q_logits[..., 1]), conf
+
+class GLPS_ACTV1(nn.Module):
+    """ACT-style wrapper that mixes Q-halt with GLPS confidence."""
+    def __init__(self, config_dict: dict):
+        super().__init__()
+        self.config = GLPS_ACTV1Config(**config_dict)
+        self.inner = GLPS_ACTV1_Inner(self.config)
+
+    @property
+    def puzzle_emb(self):
+        return self.inner.puzzle_emb
+
+    def initial_carry(self, batch: Dict[str, torch.Tensor]):
+        batch_size = batch["inputs"].shape[0]
+        return GLPS_ACTV1Carry(
+            inner_carry=self.inner.empty_carry(batch_size),
+            steps=torch.zeros((batch_size,), dtype=torch.int32),
+            halted=torch.ones((batch_size,), dtype=torch.bool),  # start halted to force reset on first pass
+            current_data={k: torch.empty_like(v) for k, v in batch.items()}
+        )
+
+    def forward(self, carry: GLPS_ACTV1Carry, batch: Dict[str, torch.Tensor]):
+        # Reset halted seqs
+        new_inner_carry = self.inner.reset_carry(carry.halted, carry.inner_carry)
+        new_steps = torch.where(carry.halted, 0, carry.steps)
+        new_current_data = {k: torch.where(carry.halted.view((-1,) + (1,) * (batch[k].ndim - 1)), batch[k], v) for k, v in carry.current_data.items()}
+
+        # Inner step
+        new_inner_carry, logits, (q_halt_logits, q_continue_logits), conf = self.inner(new_inner_carry, new_current_data)
+
+        outputs = {
+            "logits": logits,
+            "q_halt_logits": q_halt_logits,
+            "q_continue_logits": q_continue_logits,
+            "conf": conf.squeeze(-1),
+        }
+
+        with torch.no_grad():
+            new_steps = new_steps + 1
+            is_last_step = new_steps >= self.config.halt_max_steps
+
+            # Combine halt signals: Q or confidence or last-step
+            halted = is_last_step | (q_halt_logits > q_continue_logits) | (conf.squeeze(-1) >= self.config.glps_tau_halt)
+
+            # Exploration during training only
+            if self.training and (self.config.halt_max_steps > 1):
+                min_halt_steps = (torch.rand_like(q_halt_logits) < self.config.halt_exploration_prob) * torch.randint_like(new_steps, low=2, high=self.config.halt_max_steps + 1)
+                halted = halted & (new_steps >= min_halt_steps)
+                
+                # Optional target for Q-learning (kept similar to HRM)
+                next_conf = self.inner(new_inner_carry, new_current_data)[-1]
+                outputs["target_q_continue"] = torch.sigmoid(torch.where(is_last_step, q_halt_logits, torch.maximum(q_halt_logits, q_continue_logits)))
+            else:
+                # During eval, always use max_steps to ensure consistent reasoning depth (same as TRM/HRM eval behavior)
+                halted = is_last_step
+
+        return GLPS_ACTV1Carry(new_inner_carry, new_steps, halted, new_current_data), outputs
diff --git a/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_e10k_evalboost_extreme/losses.py b/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_e10k_evalboost_extreme/losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..cdfb77718bc16f98cdd7da06eb5795c36b866aa7
--- /dev/null
+++ b/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_e10k_evalboost_extreme/losses.py
@@ -0,0 +1,102 @@
+from typing import Any, Tuple, Dict, Sequence, Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+import math
+
+IGNORE_LABEL_ID = -100
+
+
+def s(x, epsilon=1e-30):
+    return torch.where(
+        x<0,
+        1/(1-x+ epsilon),
+        x + 1
+    )
+
+
+def log_stablemax(x, dim=-1):
+    s_x = s(x)
+    return torch.log(s_x/torch.sum(s_x, dim=dim, keepdim=True))
+
+
+def stablemax_cross_entropy(logits, labels, ignore_index: int = -100, valid_mask=None):
+    logprobs = log_stablemax(logits.to(torch.float64), dim=-1)
+
+    if valid_mask is None:
+        valid_mask = (labels != ignore_index)
+    transformed_labels = torch.where(valid_mask, labels, 0)
+    prediction_logprobs = torch.gather(logprobs, index=transformed_labels.to(torch.long).unsqueeze(-1), dim=-1).squeeze(-1)
+
+    return -torch.where(valid_mask, prediction_logprobs, 0)
+
+
+def softmax_cross_entropy(logits, labels, ignore_index: int = -100):
+    # Cast logits to f32
+    # Flatten logits
+    return F.cross_entropy(logits.to(torch.float32).view(-1, logits.shape[-1]), labels.to(torch.long).view(-1), ignore_index=ignore_index, reduction="none").view(labels.shape)
+
+
+class ACTLossHead(nn.Module):
+    def __init__(self, model: nn.Module, loss_type: str):
+        super().__init__()
+        self.model = model
+        self.loss_fn = globals()[loss_type]
+        
+    def initial_carry(self, *args, **kwargs):
+        return self.model.initial_carry(*args, **kwargs)  # type: ignore
+
+    def forward(
+        self,
+        return_keys: Sequence[str],
+        # Model args
+        **model_kwargs,
+    ) -> Tuple[Any, torch.Tensor, Dict[str, torch.Tensor], Optional[Dict[str, torch.Tensor]], torch.Tensor]:
+        # Model logits
+        # B x SeqLen x D
+        new_carry, outputs = self.model(**model_kwargs)
+        labels = new_carry.current_data["labels"]
+
+        with torch.no_grad():
+            # Preds
+            outputs["preds"] = torch.argmax(outputs["logits"], dim=-1)
+
+            # Correctness
+            mask = (labels != IGNORE_LABEL_ID)
+            loss_counts = mask.sum(-1)
+            loss_divisor = loss_counts.clamp_min(1).unsqueeze(-1)  # Avoid NaNs in division
+
+            is_correct = mask & (torch.argmax(outputs["logits"], dim=-1) == labels)
+            seq_is_correct = is_correct.sum(-1) == loss_counts
+            
+            # Metrics (halted)
+            valid_metrics = new_carry.halted & (loss_counts > 0)
+            metrics = {
+                "count": valid_metrics.sum(),
+                
+                "accuracy":       torch.where(valid_metrics, (is_correct.to(torch.float32) / loss_divisor).sum(-1), 0).sum(),
+                "exact_accuracy": (valid_metrics & seq_is_correct).sum(),
+
+                "q_halt_accuracy": (valid_metrics & ((outputs["q_halt_logits"] >= 0) == seq_is_correct)).sum(),
+                "steps":          torch.where(valid_metrics, new_carry.steps, 0).sum(),
+            }
+
+        # Losses
+
+        lm_loss = (self.loss_fn(outputs["logits"], labels, ignore_index=IGNORE_LABEL_ID, valid_mask=mask) / loss_divisor).sum()
+        q_halt_loss = F.binary_cross_entropy_with_logits(outputs["q_halt_logits"], seq_is_correct.to(outputs["q_halt_logits"].dtype), reduction="sum")
+        metrics.update({
+            "lm_loss": lm_loss.detach(),
+            "q_halt_loss": q_halt_loss.detach(),
+        })
+        # Q continue (bootstrapping target loss); Alexia: This fits Q-learning, but seems totally unecessary
+        q_continue_loss = 0
+        if "target_q_continue" in outputs:
+            q_continue_loss = F.binary_cross_entropy_with_logits(outputs["q_continue_logits"], outputs["target_q_continue"], reduction="sum")
+
+            metrics["q_continue_loss"] = q_continue_loss.detach()
+        # Filter outputs for return
+        detached_outputs = {k: outputs[k].detach() for k in return_keys if k in outputs}
+
+        return new_carry, lm_loss + 0.5 * (q_halt_loss + q_continue_loss), metrics, detached_outputs, new_carry.halted.all()
diff --git a/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_e10k_evalboost_ultra/all_config.yaml b/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_e10k_evalboost_ultra/all_config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..786587e2ee83bc8e9f763df9566a1d32e14ca0cc
--- /dev/null
+++ b/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_e10k_evalboost_ultra/all_config.yaml
@@ -0,0 +1,60 @@
+arch:
+  H_cycles: 5
+  H_layers: 2
+  L_cycles: 2
+  L_layers: 7
+  dep_rank: 64
+  dep_topk: 12
+  expansion: 4.0
+  forward_dtype: bfloat16
+  glps_dep_graph: true
+  glps_enabled: true
+  glps_fill_obvious: true
+  glps_global_propagate_on_low_conf: true
+  glps_max_targeted_iters: 4
+  glps_tau_halt: 0.95
+  glps_tau_uncertain: 0.7
+  glps_token_masking: true
+  halt_exploration_prob: 0.1
+  halt_max_steps: 16
+  hidden_size: 512
+  loss:
+    loss_type: stablemax_cross_entropy
+    name: losses@ACTLossHead
+  mlp_t: false
+  name: recursive_reasoning.glps@GLPS_ACTV1
+  num_heads: 8
+  pos_encodings: rope
+  puzzle_emb_ndim: 0
+  rms_norm_eps: 1.0e-05
+  rope_theta: 10000.0
+beta1: 0.9
+beta2: 0.95
+checkpoint_every_eval: true
+checkpoint_path: checkpoints/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_e10k_evalboost_ultra
+data_paths:
+- data/maze-30x30-hard-1k
+data_paths_test: []
+ema: true
+ema_rate: 0.999
+epochs: 10000
+eval_glps_max_targeted_iters: 16
+eval_glps_tau_halt: 0.65
+eval_halt_max_steps: 96
+eval_interval: 1000
+eval_only: true
+eval_save_outputs: []
+evaluators: []
+freeze_weights: false
+global_batch_size: 32
+load_checkpoint: checkpoints/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_e10k_b160/step_6250
+lr: 0.0001
+lr_min_ratio: 1.0
+lr_warmup_steps: 2000
+min_eval_interval: 0
+project_name: Maze-30x30-hard-1k-ACT-torch
+puzzle_emb_lr: 0.01
+puzzle_emb_weight_decay: 0.1
+run_name: pretrain_glps_maze30_v1_e10k_evalboost_ultra
+seed: 0
+weight_decay: 0.1
diff --git a/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_e10k_evalboost_ultra/glps.py b/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_e10k_evalboost_ultra/glps.py
new file mode 100644
index 0000000000000000000000000000000000000000..96e6de932e79e174cf773779293a643842e8a268
--- /dev/null
+++ b/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_e10k_evalboost_ultra/glps.py
@@ -0,0 +1,409 @@
+
+from typing import Tuple, List, Dict
+from dataclasses import dataclass
+import math
+import torch
+import torch.nn.functional as F
+from torch import nn
+from pydantic import BaseModel
+
+# Reuse the same building blocks as HRM/TRM
+from models.common import trunc_normal_init_
+from models.layers import rms_norm, SwiGLU, Attention, RotaryEmbedding, CosSin, CastedEmbedding, CastedLinear
+from models.sparse_embedding import CastedSparseEmbedding
+
+"""
+Global-Local Predictive Solver (GLPS)
+------------------------------------
+A light-weight control-policy on top of the HRM/TRM style blocks:
+- H1: global scan -> certainty map
+- L1: fill-obvious (lock stable cells)
+- H2: dependency scoring over remaining cells
+- L2: targeted refinement (masked updates)
+- H3: energy-based confidence -> (optional) one global propagate sweep -> halt
+
+This file keeps parameter growth tiny: a few heads + (optional) low-rank dependency scorer.
+"""
+
+@dataclass
+class GLPS_ACTV1InnerCarry:
+    z_H: torch.Tensor
+    z_L: torch.Tensor
+
+@dataclass
+class GLPS_ACTV1Carry:
+    inner_carry: GLPS_ACTV1InnerCarry
+    steps: torch.Tensor
+    halted: torch.Tensor
+    current_data: Dict[str, torch.Tensor]
+
+class GLPS_ACTV1Config(BaseModel):
+    # Core IO / shapes
+    batch_size: int
+    seq_len: int
+    puzzle_emb_ndim: int = 0
+    num_puzzle_identifiers: int = 1
+    vocab_size: int = 256
+
+    # Cycle schedule
+    H_cycles: int = 3              # (scan -> refine -> check) typical
+    L_cycles: int = 1
+
+    # Depth
+    H_layers: int = 2
+    L_layers: int = 4
+
+    # Transformer config
+    hidden_size: int = 512
+    expansion: float = 2.0
+    num_heads: int = 8
+    pos_encodings: str = "rope"
+
+    rms_norm_eps: float = 1e-5
+    rope_theta: float = 10000.0
+
+    # ACT wrapper
+    halt_max_steps: int = 4
+    halt_exploration_prob: float = 0.1
+
+    forward_dtype: str = "bfloat16"
+
+    # Optional: use MLP on L instead of attention (matches HRM/TRM option)
+    mlp_t: bool = False
+
+    # ---- GLPS extras (tiny) ----
+    glps_enabled: bool = True
+    glps_fill_obvious: bool = True
+    glps_dep_graph: bool = True
+    glps_token_masking: bool = True
+    glps_global_propagate_on_low_conf: bool = True
+
+    glps_tau_halt: float = 0.95       # final confidence to halt
+    glps_tau_uncertain: float = 0.60  # cell-wise certainty threshold
+    glps_max_targeted_iters: int = 2  # small number: 1-2
+
+    # Dependency scorer (low rank bilinear)
+    dep_rank: int = 32
+    dep_topk: int = 8
+
+class GLPSBlock(nn.Module):
+    def __init__(self, config: GLPS_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        if self.config.mlp_t:
+            self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size)
+            self.mlp_t = SwiGLU(
+                hidden_size=self.config.seq_len + self.puzzle_emb_len, # treat sequence as channel
+                expansion=config.expansion,
+            )
+        else:
+            self.self_attn = Attention(
+                hidden_size=config.hidden_size,
+                head_dim=config.hidden_size // config.num_heads,
+                num_heads=config.num_heads,
+                num_key_value_heads=config.num_heads,
+                causal=False,
+            )
+        self.mlp = SwiGLU(hidden_size=config.hidden_size, expansion=config.expansion)
+        self.norm_eps = config.rms_norm_eps
+
+    def forward(self, cos_sin: CosSin, hidden_states: torch.Tensor) -> torch.Tensor:
+        if self.config.mlp_t:
+            # MLP over sequence dimension (mlp-t)
+            hidden_states = hidden_states.transpose(1, 2)
+            out = self.mlp_t(hidden_states)
+            hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+            hidden_states = hidden_states.transpose(1, 2)
+        else:
+            hidden_states = rms_norm(
+                hidden_states + self.self_attn(cos_sin=cos_sin, hidden_states=hidden_states),
+                variance_epsilon=self.norm_eps,
+            )
+        out = self.mlp(hidden_states)
+        hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+        return hidden_states
+
+class GLPSReasoningModule(nn.Module):
+    """Reasoning stack with optional masked updates (only update uncertain tokens)."""
+    def __init__(self, layers: List[GLPSBlock]):
+        super().__init__()
+        self.layers = torch.nn.ModuleList(layers)
+
+    def forward(self, hidden_states: torch.Tensor, input_injection: torch.Tensor, update_mask: torch.Tensor = None, **kwargs) -> torch.Tensor:
+        x = hidden_states
+        for layer in self.layers:
+            # Compute candidate update using injected context
+            y = layer(hidden_states=x + input_injection, **kwargs)
+            if update_mask is not None:
+                # Convex blend keeps frozen tokens unchanged
+                m = update_mask.to(x.dtype)[..., None]
+                x = x + m * (y - x)
+            else:
+                x = y
+        return x
+
+class GLPS_ACTV1_Inner(nn.Module):
+    def __init__(self, config: GLPS_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        self.forward_dtype = getattr(torch, self.config.forward_dtype)
+
+        # I/O
+        self.embed_scale  = math.sqrt(self.config.hidden_size)
+        embed_init_std = 1.0 / self.embed_scale
+
+        self.embed_tokens = CastedEmbedding(self.config.vocab_size, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+        self.lm_head      = CastedLinear(self.config.hidden_size, self.config.vocab_size, bias=False)
+        self.q_head       = CastedLinear(self.config.hidden_size, 2, bias=True)
+
+        # Puzzle emb (optional) — same convention as HRM/TRM
+        self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size)  # ceil div
+        if self.config.puzzle_emb_ndim > 0:
+            self.puzzle_emb = CastedSparseEmbedding(self.config.num_puzzle_identifiers, self.config.puzzle_emb_ndim, batch_size=self.config.batch_size, init_std=0, cast_to=self.forward_dtype)
+
+        # Positional encodings
+        if self.config.pos_encodings == "rope":
+            self.rotary_emb = RotaryEmbedding(dim=self.config.hidden_size // self.config.num_heads, max_position_embeddings=self.config.seq_len + self.puzzle_emb_len, base=self.config.rope_theta)
+        elif self.config.pos_encodings == "learned":
+            self.embed_pos = CastedEmbedding(self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+
+        # Reasoning stacks
+        self.H_level = GLPSReasoningModule(layers=[GLPSBlock(self.config) for _ in range(self.config.H_layers)])
+        self.L_level = GLPSReasoningModule(layers=[GLPSBlock(self.config) for _ in range(self.config.L_layers)])
+
+        # Initial states (match HRM/TRM style)
+        H_init = trunc_normal_init_(torch.empty(1, 1, self.config.hidden_size, dtype=self.forward_dtype), std=1.0)
+        L_init = trunc_normal_init_(torch.empty(1, 1, self.config.hidden_size, dtype=self.forward_dtype), std=1.0)
+        self.register_buffer("H_init", H_init, persistent=True)
+        self.register_buffer("L_init", L_init, persistent=True)
+
+        # GLPS small heads
+        self.candidate_head = CastedLinear(self.config.hidden_size, 16, bias=True)  # task-specific; for Sudoku you can slice to 9
+        self.certainty_head = CastedLinear(self.config.hidden_size, 1, bias=True)
+        self.energy_head    = CastedLinear(self.config.hidden_size, 1, bias=True)
+
+        # Low-rank dependency scorer (shared)
+        r = max(1, self.config.dep_rank)
+        self.dep_q = CastedLinear(self.config.hidden_size, r, bias=False)
+        self.dep_k = CastedLinear(self.config.hidden_size, r, bias=False)
+
+        # Q head init like HRM/TRM (near-zero -> easier bootstrapping)
+        with torch.no_grad():
+            self.q_head.weight.zero_()
+            self.q_head.bias.fill_(-5)
+
+    def _input_embeddings(self, input: torch.Tensor, puzzle_identifiers: torch.Tensor):
+        # Token embedding
+        embedding = self.embed_tokens(input.to(torch.int32))
+
+        # Puzzle embeddings
+        if self.config.puzzle_emb_ndim > 0:
+            puzzle_embedding = self.puzzle_emb(puzzle_identifiers)
+            pad_count = self.puzzle_emb_len * self.config.hidden_size - puzzle_embedding.shape[-1]
+            if pad_count > 0:
+                puzzle_embedding = F.pad(puzzle_embedding, (0, pad_count))
+            embedding = torch.cat((puzzle_embedding.view(-1, self.puzzle_emb_len, self.config.hidden_size), embedding), dim=-2)
+
+        # Position embeddings
+        if self.config.pos_encodings == "learned":
+            embedding = 0.707106781 * (embedding + self.embed_pos.embedding_weight.to(self.forward_dtype))
+
+        return self.embed_scale * embedding
+
+    def empty_carry(self, batch_size: int):
+        return GLPS_ACTV1InnerCarry(
+            z_H=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+            z_L=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+        )
+
+    def reset_carry(self, reset_flag: torch.Tensor, carry: GLPS_ACTV1InnerCarry):
+        # Explicitly expand buffers and mask to target shapes to avoid shape confusion
+        B, L, D = carry.z_H.shape
+        # Reduce/reset flag to per-batch boolean vector of shape [B]
+        if reset_flag.ndim == 1 and reset_flag.shape[0] == B:
+            reset_b = reset_flag.to(torch.bool)
+        else:
+            # If shape is [B, ...] reduce across non-batch dims; otherwise fallback to first B entries
+            try:
+                reset_b = reset_flag.reshape(B, -1).any(dim=1).to(torch.bool)
+            except Exception:
+                reset_b = reset_flag.reshape(-1)[:B].to(torch.bool)
+        m = reset_b.view(B, 1, 1)
+        mH = m.expand(B, L, D)
+        mL = mH  # same shape for z_L
+        H_init_exp = self.H_init.expand(B, L, D)
+        L_init_exp = self.L_init.expand(B, L, D)
+        return GLPS_ACTV1InnerCarry(
+            z_H=torch.where(mH, H_init_exp, carry.z_H),
+            z_L=torch.where(mL, L_init_exp, carry.z_L),
+        )
+
+    def _global_scan(self, z_L, z_H, input_embeddings, seq_info):
+        # One light pass to gather global signals
+        z_scan = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+        cand_logits = self.candidate_head(z_scan)              # [B, L, C]
+        certainty   = torch.sigmoid(self.certainty_head(z_scan))  # [B, L, 1]
+        return z_scan, cand_logits, certainty
+
+    def _build_dep_mask(self, z_ctx, uncertain_mask: torch.Tensor):
+        """Compute a dependency-based focus mask from a low-rank bilinear score.
+        uncertain_mask: [B, L] boolean mask of cells that are currently uncertain
+        Returns: dep_mask [B, L] boolean mask of cells to (re)update.
+        """
+        B, L, D = z_ctx.shape
+        # Project to low-rank space; use float32 to avoid dtype mismatches under torch.compile
+        Q = self.dep_q(z_ctx).to(torch.float32)   # [B, L, r]
+        K = self.dep_k(z_ctx).to(torch.float32)   # [B, L, r]
+        r = max(1, int(Q.shape[-1]))
+        sim = torch.matmul(Q, K.transpose(1, 2))  # [B, L, L] (float32)
+        sim = sim / math.sqrt(r)
+
+        # Aggregate influence from uncertain queries onto target tokens
+        src = uncertain_mask.to(sim.dtype).unsqueeze(1)      # [B, 1, L]
+        influence = torch.matmul(src, sim).squeeze(1)        # [B, L]
+
+        # Top-k influenced tokens per batch
+        topk = min(self.config.dep_topk, L)
+        vals, idx = torch.topk(influence, k=topk, dim=-1)    # [B, topk]
+        dep_mask = torch.zeros_like(uncertain_mask)
+        dep_mask.scatter_(1, idx, True)
+
+        # Always include uncertain cells themselves
+        dep_mask = dep_mask | uncertain_mask
+        return dep_mask
+
+    def forward(self, carry: GLPS_ACTV1InnerCarry, batch: Dict[str, torch.Tensor]):
+        seq_info = dict(cos_sin=getattr(self, "rotary_emb", None)() if hasattr(self, "rotary_emb") else None)
+
+        # Encode inputs
+        input_embeddings = self._input_embeddings(batch["inputs"], batch["puzzle_identifiers"])
+
+        # States
+        z_H, z_L = carry.z_H, carry.z_L
+
+        if not self.config.glps_enabled:
+            # Fallback to an HRM-like single-cycle grad update for compatibility
+            with torch.no_grad():
+                for _H in range(self.config.H_cycles - 1):
+                    for _L in range(self.config.L_cycles):
+                        z_L = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+                    z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+            # final grad step
+            for _L in range(self.config.L_cycles):
+                z_L = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+            z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+
+            # Outputs
+            new_carry = GLPS_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach())
+            logits = self.lm_head(z_H)[:, self.puzzle_emb_len:]
+            q_logits = self.q_head(z_H[:, 0]).to(torch.float32)
+            conf = torch.zeros_like(q_logits[..., :1]) + 0.5  # neutral
+            return new_carry, logits, (q_logits[..., 0], q_logits[..., 1]), conf
+
+        # ===== GLPS path =====
+        # H1: global scan (cheap)
+        with torch.no_grad():
+            z_scan, cand_logits, certainty = self._global_scan(z_L, z_H, input_embeddings, seq_info)
+
+        # L1: fill-obvious -> compute stable vs uncertain masks
+        if self.config.glps_fill_obvious:
+            obvious_mask = (certainty >= self.config.glps_tau_uncertain)  # [B, L, 1]
+        else:
+            obvious_mask = torch.zeros_like(certainty).bool()
+        stable_mask = obvious_mask.squeeze(-1)         # [B, L]
+        uncertain_mask = ~stable_mask                  # [B, L]
+
+        # H2: dependency prediction over remaining cells
+        if self.config.glps_dep_graph:
+            dep_mask = self._build_dep_mask(z_scan, uncertain_mask)   # [B, L]
+        else:
+            dep_mask = uncertain_mask
+
+        # L2: targeted refinement (a couple of masked iters)
+        update_mask = dep_mask if self.config.glps_token_masking else None
+        z = z_scan.detach()  # use scanned context as start
+        for _ in range(self.config.glps_max_targeted_iters):
+            z = self.L_level(z, z_H + input_embeddings, update_mask=update_mask, **seq_info)
+            # Refresh certainty to shrink mask (optional but cheap)
+            cert_now = torch.sigmoid(self.certainty_head(z)).squeeze(-1)
+            if self.config.glps_token_masking:
+                update_mask = dep_mask & (cert_now < self.config.glps_tau_uncertain)
+
+        # Merge into H and do a light H update with grad
+        z_L = z
+        z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+
+        # H3: energy/consistency -> confidence & optional global propagate
+        energy = torch.sigmoid(self.energy_head(z_H)).mean(dim=1)  # [B, 1]
+        conf = 1.0 - energy
+        need_sweep = (conf.squeeze(-1) < self.config.glps_tau_halt)
+        if self.config.glps_global_propagate_on_low_conf and need_sweep.any():
+            # one final full sweep only for rows needing it
+            maskB = need_sweep.view(-1, 1, 1)
+            zL2 = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+            zH2 = self.H_level(z_H, zL2, update_mask=None, **seq_info)
+            z_L = torch.where(maskB, zL2, z_L)
+            z_H = torch.where(maskB, zH2, z_H)
+
+        # Outputs
+        new_carry = GLPS_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach())
+        logits = self.lm_head(z_H)[:, self.puzzle_emb_len:]
+        q_logits = self.q_head(z_H[:, 0]).to(torch.float32)
+        return new_carry, logits, (q_logits[..., 0], q_logits[..., 1]), conf
+
+class GLPS_ACTV1(nn.Module):
+    """ACT-style wrapper that mixes Q-halt with GLPS confidence."""
+    def __init__(self, config_dict: dict):
+        super().__init__()
+        self.config = GLPS_ACTV1Config(**config_dict)
+        self.inner = GLPS_ACTV1_Inner(self.config)
+
+    @property
+    def puzzle_emb(self):
+        return self.inner.puzzle_emb
+
+    def initial_carry(self, batch: Dict[str, torch.Tensor]):
+        batch_size = batch["inputs"].shape[0]
+        return GLPS_ACTV1Carry(
+            inner_carry=self.inner.empty_carry(batch_size),
+            steps=torch.zeros((batch_size,), dtype=torch.int32),
+            halted=torch.ones((batch_size,), dtype=torch.bool),  # start halted to force reset on first pass
+            current_data={k: torch.empty_like(v) for k, v in batch.items()}
+        )
+
+    def forward(self, carry: GLPS_ACTV1Carry, batch: Dict[str, torch.Tensor]):
+        # Reset halted seqs
+        new_inner_carry = self.inner.reset_carry(carry.halted, carry.inner_carry)
+        new_steps = torch.where(carry.halted, 0, carry.steps)
+        new_current_data = {k: torch.where(carry.halted.view((-1,) + (1,) * (batch[k].ndim - 1)), batch[k], v) for k, v in carry.current_data.items()}
+
+        # Inner step
+        new_inner_carry, logits, (q_halt_logits, q_continue_logits), conf = self.inner(new_inner_carry, new_current_data)
+
+        outputs = {
+            "logits": logits,
+            "q_halt_logits": q_halt_logits,
+            "q_continue_logits": q_continue_logits,
+            "conf": conf.squeeze(-1),
+        }
+
+        with torch.no_grad():
+            new_steps = new_steps + 1
+            is_last_step = new_steps >= self.config.halt_max_steps
+
+            # Combine halt signals: Q or confidence or last-step
+            halted = is_last_step | (q_halt_logits > q_continue_logits) | (conf.squeeze(-1) >= self.config.glps_tau_halt)
+
+            # Exploration during training only
+            if self.training and (self.config.halt_max_steps > 1):
+                min_halt_steps = (torch.rand_like(q_halt_logits) < self.config.halt_exploration_prob) * torch.randint_like(new_steps, low=2, high=self.config.halt_max_steps + 1)
+                halted = halted & (new_steps >= min_halt_steps)
+                
+                # Optional target for Q-learning (kept similar to HRM)
+                next_conf = self.inner(new_inner_carry, new_current_data)[-1]
+                outputs["target_q_continue"] = torch.sigmoid(torch.where(is_last_step, q_halt_logits, torch.maximum(q_halt_logits, q_continue_logits)))
+            else:
+                # During eval, always use max_steps to ensure consistent reasoning depth (same as TRM/HRM eval behavior)
+                halted = is_last_step
+
+        return GLPS_ACTV1Carry(new_inner_carry, new_steps, halted, new_current_data), outputs
diff --git a/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_e10k_evalboost_ultra/losses.py b/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_e10k_evalboost_ultra/losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..cdfb77718bc16f98cdd7da06eb5795c36b866aa7
--- /dev/null
+++ b/Maze-30x30-hard-1k-ACT-torch/pretrain_glps_maze30_v1_e10k_evalboost_ultra/losses.py
@@ -0,0 +1,102 @@
+from typing import Any, Tuple, Dict, Sequence, Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+import math
+
+IGNORE_LABEL_ID = -100
+
+
+def s(x, epsilon=1e-30):
+    return torch.where(
+        x<0,
+        1/(1-x+ epsilon),
+        x + 1
+    )
+
+
+def log_stablemax(x, dim=-1):
+    s_x = s(x)
+    return torch.log(s_x/torch.sum(s_x, dim=dim, keepdim=True))
+
+
+def stablemax_cross_entropy(logits, labels, ignore_index: int = -100, valid_mask=None):
+    logprobs = log_stablemax(logits.to(torch.float64), dim=-1)
+
+    if valid_mask is None:
+        valid_mask = (labels != ignore_index)
+    transformed_labels = torch.where(valid_mask, labels, 0)
+    prediction_logprobs = torch.gather(logprobs, index=transformed_labels.to(torch.long).unsqueeze(-1), dim=-1).squeeze(-1)
+
+    return -torch.where(valid_mask, prediction_logprobs, 0)
+
+
+def softmax_cross_entropy(logits, labels, ignore_index: int = -100):
+    # Cast logits to f32
+    # Flatten logits
+    return F.cross_entropy(logits.to(torch.float32).view(-1, logits.shape[-1]), labels.to(torch.long).view(-1), ignore_index=ignore_index, reduction="none").view(labels.shape)
+
+
+class ACTLossHead(nn.Module):
+    def __init__(self, model: nn.Module, loss_type: str):
+        super().__init__()
+        self.model = model
+        self.loss_fn = globals()[loss_type]
+        
+    def initial_carry(self, *args, **kwargs):
+        return self.model.initial_carry(*args, **kwargs)  # type: ignore
+
+    def forward(
+        self,
+        return_keys: Sequence[str],
+        # Model args
+        **model_kwargs,
+    ) -> Tuple[Any, torch.Tensor, Dict[str, torch.Tensor], Optional[Dict[str, torch.Tensor]], torch.Tensor]:
+        # Model logits
+        # B x SeqLen x D
+        new_carry, outputs = self.model(**model_kwargs)
+        labels = new_carry.current_data["labels"]
+
+        with torch.no_grad():
+            # Preds
+            outputs["preds"] = torch.argmax(outputs["logits"], dim=-1)
+
+            # Correctness
+            mask = (labels != IGNORE_LABEL_ID)
+            loss_counts = mask.sum(-1)
+            loss_divisor = loss_counts.clamp_min(1).unsqueeze(-1)  # Avoid NaNs in division
+
+            is_correct = mask & (torch.argmax(outputs["logits"], dim=-1) == labels)
+            seq_is_correct = is_correct.sum(-1) == loss_counts
+            
+            # Metrics (halted)
+            valid_metrics = new_carry.halted & (loss_counts > 0)
+            metrics = {
+                "count": valid_metrics.sum(),
+                
+                "accuracy":       torch.where(valid_metrics, (is_correct.to(torch.float32) / loss_divisor).sum(-1), 0).sum(),
+                "exact_accuracy": (valid_metrics & seq_is_correct).sum(),
+
+                "q_halt_accuracy": (valid_metrics & ((outputs["q_halt_logits"] >= 0) == seq_is_correct)).sum(),
+                "steps":          torch.where(valid_metrics, new_carry.steps, 0).sum(),
+            }
+
+        # Losses
+
+        lm_loss = (self.loss_fn(outputs["logits"], labels, ignore_index=IGNORE_LABEL_ID, valid_mask=mask) / loss_divisor).sum()
+        q_halt_loss = F.binary_cross_entropy_with_logits(outputs["q_halt_logits"], seq_is_correct.to(outputs["q_halt_logits"].dtype), reduction="sum")
+        metrics.update({
+            "lm_loss": lm_loss.detach(),
+            "q_halt_loss": q_halt_loss.detach(),
+        })
+        # Q continue (bootstrapping target loss); Alexia: This fits Q-learning, but seems totally unecessary
+        q_continue_loss = 0
+        if "target_q_continue" in outputs:
+            q_continue_loss = F.binary_cross_entropy_with_logits(outputs["q_continue_logits"], outputs["target_q_continue"], reduction="sum")
+
+            metrics["q_continue_loss"] = q_continue_loss.detach()
+        # Filter outputs for return
+        detached_outputs = {k: outputs[k].detach() for k in return_keys if k in outputs}
+
+        return new_carry, lm_loss + 0.5 * (q_halt_loss + q_continue_loss), metrics, detached_outputs, new_carry.halted.all()
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku/all_config.yaml b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku/all_config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3ba39d56bc9146edb32bba8b2e1e695e90c047c6
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku/all_config.yaml
@@ -0,0 +1,56 @@
+arch:
+  H_cycles: 3
+  H_layers: 2
+  L_cycles: 1
+  L_layers: 4
+  dep_rank: 32
+  dep_topk: 8
+  expansion: 2.0
+  forward_dtype: bfloat16
+  glps_dep_graph: true
+  glps_enabled: true
+  glps_fill_obvious: true
+  glps_global_propagate_on_low_conf: true
+  glps_max_targeted_iters: 2
+  glps_tau_halt: 0.95
+  glps_tau_uncertain: 0.6
+  glps_token_masking: true
+  halt_exploration_prob: 0.1
+  halt_max_steps: 4
+  hidden_size: 512
+  loss:
+    loss_type: stablemax_cross_entropy
+    name: losses@ACTLossHead
+  mlp_t: false
+  name: recursive_reasoning.glps@GLPS_ACTV1
+  num_heads: 8
+  pos_encodings: rope
+  puzzle_emb_ndim: 0
+  rms_norm_eps: 1.0e-05
+  rope_theta: 10000.0
+beta1: 0.9
+beta2: 0.95
+checkpoint_every_eval: true
+checkpoint_path: checkpoints/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku
+data_paths:
+- data/sudoku-extreme-1k-aug-1000
+data_paths_test: []
+ema: true
+ema_rate: 0.999
+epochs: 50000
+eval_interval: 5000
+eval_save_outputs: []
+evaluators: []
+freeze_weights: false
+global_batch_size: 768
+load_checkpoint: null
+lr: 0.0001
+lr_min_ratio: 1.0
+lr_warmup_steps: 2000
+min_eval_interval: 0
+project_name: Sudoku-extreme-1k-aug-1000-ACT-torch
+puzzle_emb_lr: 0.0001
+puzzle_emb_weight_decay: 1.0
+run_name: pretrain_glps_sudoku
+seed: 0
+weight_decay: 1.0
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku/glps.py b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku/glps.py
new file mode 100644
index 0000000000000000000000000000000000000000..009c5339fb02597e39d147d9c531b6159e3ae244
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku/glps.py
@@ -0,0 +1,390 @@
+
+from typing import Tuple, List, Dict
+from dataclasses import dataclass
+import math
+import torch
+import torch.nn.functional as F
+from torch import nn
+from pydantic import BaseModel
+
+# Reuse the same building blocks as HRM/TRM
+from models.common import trunc_normal_init_
+from models.layers import rms_norm, SwiGLU, Attention, RotaryEmbedding, CosSin, CastedEmbedding, CastedLinear
+from models.sparse_embedding import CastedSparseEmbedding
+
+"""
+Global-Local Predictive Solver (GLPS)
+------------------------------------
+A light-weight control-policy on top of the HRM/TRM style blocks:
+- H1: global scan -> certainty map
+- L1: fill-obvious (lock stable cells)
+- H2: dependency scoring over remaining cells
+- L2: targeted refinement (masked updates)
+- H3: energy-based confidence -> (optional) one global propagate sweep -> halt
+
+This file keeps parameter growth tiny: a few heads + (optional) low-rank dependency scorer.
+"""
+
+@dataclass
+class GLPS_ACTV1InnerCarry:
+    z_H: torch.Tensor
+    z_L: torch.Tensor
+
+@dataclass
+class GLPS_ACTV1Carry:
+    inner_carry: GLPS_ACTV1InnerCarry
+    steps: torch.Tensor
+    halted: torch.Tensor
+    current_data: Dict[str, torch.Tensor]
+
+class GLPS_ACTV1Config(BaseModel):
+    # Core IO / shapes
+    batch_size: int
+    seq_len: int
+    puzzle_emb_ndim: int = 0
+    num_puzzle_identifiers: int = 1
+    vocab_size: int = 256
+
+    # Cycle schedule
+    H_cycles: int = 3              # (scan -> refine -> check) typical
+    L_cycles: int = 1
+
+    # Depth
+    H_layers: int = 2
+    L_layers: int = 4
+
+    # Transformer config
+    hidden_size: int = 512
+    expansion: float = 2.0
+    num_heads: int = 8
+    pos_encodings: str = "rope"
+
+    rms_norm_eps: float = 1e-5
+    rope_theta: float = 10000.0
+
+    # ACT wrapper
+    halt_max_steps: int = 4
+    halt_exploration_prob: float = 0.1
+
+    forward_dtype: str = "bfloat16"
+
+    # Optional: use MLP on L instead of attention (matches HRM/TRM option)
+    mlp_t: bool = False
+
+    # ---- GLPS extras (tiny) ----
+    glps_enabled: bool = True
+    glps_fill_obvious: bool = True
+    glps_dep_graph: bool = True
+    glps_token_masking: bool = True
+    glps_global_propagate_on_low_conf: bool = True
+
+    glps_tau_halt: float = 0.95       # final confidence to halt
+    glps_tau_uncertain: float = 0.60  # cell-wise certainty threshold
+    glps_max_targeted_iters: int = 2  # small number: 1-2
+
+    # Dependency scorer (low rank bilinear)
+    dep_rank: int = 32
+    dep_topk: int = 8
+
+class GLPSBlock(nn.Module):
+    def __init__(self, config: GLPS_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        if self.config.mlp_t:
+            self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size)
+            self.mlp_t = SwiGLU(
+                hidden_size=self.config.seq_len + self.puzzle_emb_len, # treat sequence as channel
+                expansion=config.expansion,
+            )
+        else:
+            self.self_attn = Attention(
+                hidden_size=config.hidden_size,
+                head_dim=config.hidden_size // config.num_heads,
+                num_heads=config.num_heads,
+                num_key_value_heads=config.num_heads,
+                causal=False,
+            )
+        self.mlp = SwiGLU(hidden_size=config.hidden_size, expansion=config.expansion)
+        self.norm_eps = config.rms_norm_eps
+
+    def forward(self, cos_sin: CosSin, hidden_states: torch.Tensor) -> torch.Tensor:
+        if self.config.mlp_t:
+            # MLP over sequence dimension (mlp-t)
+            hidden_states = hidden_states.transpose(1, 2)
+            out = self.mlp_t(hidden_states)
+            hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+            hidden_states = hidden_states.transpose(1, 2)
+        else:
+            hidden_states = rms_norm(
+                hidden_states + self.self_attn(cos_sin=cos_sin, hidden_states=hidden_states),
+                variance_epsilon=self.norm_eps,
+            )
+        out = self.mlp(hidden_states)
+        hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+        return hidden_states
+
+class GLPSReasoningModule(nn.Module):
+    """Reasoning stack with optional masked updates (only update uncertain tokens)."""
+    def __init__(self, layers: List[GLPSBlock]):
+        super().__init__()
+        self.layers = torch.nn.ModuleList(layers)
+
+    def forward(self, hidden_states: torch.Tensor, input_injection: torch.Tensor, update_mask: torch.Tensor = None, **kwargs) -> torch.Tensor:
+        x = hidden_states
+        for layer in self.layers:
+            # Compute candidate update using injected context
+            y = layer(hidden_states=x + input_injection, **kwargs)
+            if update_mask is not None:
+                # Convex blend keeps frozen tokens unchanged
+                m = update_mask.to(x.dtype)[..., None]
+                x = x + m * (y - x)
+            else:
+                x = y
+        return x
+
+class GLPS_ACTV1_Inner(nn.Module):
+    def __init__(self, config: GLPS_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        self.forward_dtype = getattr(torch, self.config.forward_dtype)
+
+        # I/O
+        self.embed_scale  = math.sqrt(self.config.hidden_size)
+        embed_init_std = 1.0 / self.embed_scale
+
+        self.embed_tokens = CastedEmbedding(self.config.vocab_size, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+        self.lm_head      = CastedLinear(self.config.hidden_size, self.config.vocab_size, bias=False)
+        self.q_head       = CastedLinear(self.config.hidden_size, 2, bias=True)
+
+        # Puzzle emb (optional) — same convention as HRM/TRM
+        self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size)  # ceil div
+        if self.config.puzzle_emb_ndim > 0:
+            self.puzzle_emb = CastedSparseEmbedding(self.config.num_puzzle_identifiers, self.config.puzzle_emb_ndim, batch_size=self.config.batch_size, init_std=0, cast_to=self.forward_dtype)
+
+        # Positional encodings
+        if self.config.pos_encodings == "rope":
+            self.rotary_emb = RotaryEmbedding(dim=self.config.hidden_size // self.config.num_heads, max_position_embeddings=self.config.seq_len + self.puzzle_emb_len, base=self.config.rope_theta)
+        elif self.config.pos_encodings == "learned":
+            self.embed_pos = CastedEmbedding(self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+
+        # Reasoning stacks
+        self.H_level = GLPSReasoningModule(layers=[GLPSBlock(self.config) for _ in range(self.config.H_layers)])
+        self.L_level = GLPSReasoningModule(layers=[GLPSBlock(self.config) for _ in range(self.config.L_layers)])
+
+        # Initial states (match HRM/TRM style)
+        H_init = trunc_normal_init_(torch.empty(1, 1, self.config.hidden_size, dtype=self.forward_dtype), std=1.0)
+        L_init = trunc_normal_init_(torch.empty(1, 1, self.config.hidden_size, dtype=self.forward_dtype), std=1.0)
+        self.register_buffer("H_init", H_init, persistent=True)
+        self.register_buffer("L_init", L_init, persistent=True)
+
+        # GLPS small heads
+        self.candidate_head = CastedLinear(self.config.hidden_size, 16, bias=True)  # task-specific; for Sudoku you can slice to 9
+        self.certainty_head = CastedLinear(self.config.hidden_size, 1, bias=True)
+        self.energy_head    = CastedLinear(self.config.hidden_size, 1, bias=True)
+
+        # Low-rank dependency scorer (shared)
+        r = max(1, self.config.dep_rank)
+        self.dep_q = CastedLinear(self.config.hidden_size, r, bias=False)
+        self.dep_k = CastedLinear(self.config.hidden_size, r, bias=False)
+
+        # Q head init like HRM/TRM (near-zero -> easier bootstrapping)
+        with torch.no_grad():
+            self.q_head.weight.zero_()
+            self.q_head.bias.fill_(-5)
+
+    def _input_embeddings(self, input: torch.Tensor, puzzle_identifiers: torch.Tensor):
+        # Token embedding
+        embedding = self.embed_tokens(input.to(torch.int32))
+
+        # Puzzle embeddings
+        if self.config.puzzle_emb_ndim > 0:
+            puzzle_embedding = self.puzzle_emb(puzzle_identifiers)
+            pad_count = self.puzzle_emb_len * self.config.hidden_size - puzzle_embedding.shape[-1]
+            if pad_count > 0:
+                puzzle_embedding = F.pad(puzzle_embedding, (0, pad_count))
+            embedding = torch.cat((puzzle_embedding.view(-1, self.puzzle_emb_len, self.config.hidden_size), embedding), dim=-2)
+
+        # Position embeddings
+        if self.config.pos_encodings == "learned":
+            embedding = 0.707106781 * (embedding + self.embed_pos.embedding_weight.to(self.forward_dtype))
+
+        return self.embed_scale * embedding
+
+    def empty_carry(self, batch_size: int):
+        return GLPS_ACTV1InnerCarry(
+            z_H=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+            z_L=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+        )
+
+    def reset_carry(self, reset_flag: torch.Tensor, carry: GLPS_ACTV1InnerCarry):
+        m = reset_flag.view(-1, 1, 1)
+        return GLPS_ACTV1InnerCarry(
+            z_H=torch.where(m, self.H_init.expand_as(carry.z_H), carry.z_H),
+            z_L=torch.where(m, self.L_init.expand_as(carry.z_L), carry.z_L),
+        )
+
+    def _global_scan(self, z_L, z_H, input_embeddings, seq_info):
+        # One light pass to gather global signals
+        z_scan = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+        cand_logits = self.candidate_head(z_scan)              # [B, L, C]
+        certainty   = torch.sigmoid(self.certainty_head(z_scan))  # [B, L, 1]
+        return z_scan, cand_logits, certainty
+
+    def _build_dep_mask(self, z_ctx, uncertain_mask: torch.Tensor):
+        """Compute a dependency-based focus mask from a low-rank bilinear score.
+        uncertain_mask: [B, L] boolean mask of cells that are currently uncertain
+        Returns: dep_mask [B, L] boolean mask of cells to (re)update.
+        """
+        B, L, D = z_ctx.shape
+        Q = self.dep_q(z_ctx)                     # [B, L, r]
+        K = self.dep_k(z_ctx)                     # [B, L, r]
+        r = max(1, int(Q.shape[-1]))
+        sim = torch.matmul(Q, K.transpose(1, 2))  # [B, L, L]
+        sim = sim / math.sqrt(r)
+
+        # Aggregate influence from uncertain queries onto target tokens
+        src = uncertain_mask.float().unsqueeze(1)            # [B, 1, L]
+        influence = torch.matmul(src, sim).squeeze(1)        # [B, L]
+
+        # Top-k influenced tokens per batch
+        topk = min(self.config.dep_topk, L)
+        vals, idx = torch.topk(influence, k=topk, dim=-1)    # [B, topk]
+        dep_mask = torch.zeros_like(uncertain_mask)
+        dep_mask.scatter_(1, idx, True)
+
+        # Always include uncertain cells themselves
+        dep_mask = dep_mask | uncertain_mask
+        return dep_mask
+
+    def forward(self, carry: GLPS_ACTV1InnerCarry, batch: Dict[str, torch.Tensor]):
+        seq_info = dict(cos_sin=getattr(self, "rotary_emb", None)() if hasattr(self, "rotary_emb") else None)
+
+        # Encode inputs
+        input_embeddings = self._input_embeddings(batch["inputs"], batch["puzzle_identifiers"])
+
+        # States
+        z_H, z_L = carry.z_H, carry.z_L
+
+        if not self.config.glps_enabled:
+            # Fallback to an HRM-like single-cycle grad update for compatibility
+            with torch.no_grad():
+                for _H in range(self.config.H_cycles - 1):
+                    for _L in range(self.config.L_cycles):
+                        z_L = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+                    z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+            # final grad step
+            for _L in range(self.config.L_cycles):
+                z_L = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+            z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+
+            # Outputs
+            new_carry = GLPS_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach())
+            logits = self.lm_head(z_H)[:, self.puzzle_emb_len:]
+            q_logits = self.q_head(z_H[:, 0]).to(torch.float32)
+            conf = torch.zeros_like(q_logits[..., :1]) + 0.5  # neutral
+            return new_carry, logits, (q_logits[..., 0], q_logits[..., 1]), conf
+
+        # ===== GLPS path =====
+        # H1: global scan (cheap)
+        with torch.no_grad():
+            z_scan, cand_logits, certainty = self._global_scan(z_L, z_H, input_embeddings, seq_info)
+
+        # L1: fill-obvious -> compute stable vs uncertain masks
+        if self.config.glps_fill_obvious:
+            obvious_mask = (certainty >= self.config.glps_tau_uncertain)  # [B, L, 1]
+        else:
+            obvious_mask = torch.zeros_like(certainty).bool()
+        stable_mask = obvious_mask.squeeze(-1)         # [B, L]
+        uncertain_mask = ~stable_mask                  # [B, L]
+
+        # H2: dependency prediction over remaining cells
+        if self.config.glps_dep_graph:
+            dep_mask = self._build_dep_mask(z_scan, uncertain_mask)   # [B, L]
+        else:
+            dep_mask = uncertain_mask
+
+        # L2: targeted refinement (a couple of masked iters)
+        update_mask = dep_mask if self.config.glps_token_masking else None
+        z = z_scan.detach()  # use scanned context as start
+        for _ in range(self.config.glps_max_targeted_iters):
+            z = self.L_level(z, z_H + input_embeddings, update_mask=update_mask, **seq_info)
+            # Refresh certainty to shrink mask (optional but cheap)
+            cert_now = torch.sigmoid(self.certainty_head(z)).squeeze(-1)
+            if self.config.glps_token_masking:
+                update_mask = dep_mask & (cert_now < self.config.glps_tau_uncertain)
+
+        # Merge into H and do a light H update with grad
+        z_L = z
+        z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+
+        # H3: energy/consistency -> confidence & optional global propagate
+        energy = torch.sigmoid(self.energy_head(z_H)).mean(dim=1, keepdim=True)  # [B,1]
+        conf = 1.0 - energy
+        need_sweep = (conf.squeeze(-1) < self.config.glps_tau_halt)
+        if self.config.glps_global_propagate_on_low_conf and need_sweep.any():
+            # one final full sweep only for rows needing it
+            maskB = need_sweep.view(-1, 1, 1)
+            zL2 = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+            zH2 = self.H_level(z_H, zL2, update_mask=None, **seq_info)
+            z_L = torch.where(maskB, zL2, z_L)
+            z_H = torch.where(maskB, zH2, z_H)
+
+        # Outputs
+        new_carry = GLPS_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach())
+        logits = self.lm_head(z_H)[:, self.puzzle_emb_len:]
+        q_logits = self.q_head(z_H[:, 0]).to(torch.float32)
+        return new_carry, logits, (q_logits[..., 0], q_logits[..., 1]), conf
+
+class GLPS_ACTV1(nn.Module):
+    """ACT-style wrapper that mixes Q-halt with GLPS confidence."""
+    def __init__(self, config_dict: dict):
+        super().__init__()
+        self.config = GLPS_ACTV1Config(**config_dict)
+        self.inner = GLPS_ACTV1_Inner(self.config)
+
+    @property
+    def puzzle_emb(self):
+        return self.inner.puzzle_emb
+
+    def initial_carry(self, batch: Dict[str, torch.Tensor]):
+        batch_size = batch["inputs"].shape[0]
+        return GLPS_ACTV1Carry(
+            inner_carry=self.inner.empty_carry(batch_size),
+            steps=torch.zeros((batch_size,), dtype=torch.int32),
+            halted=torch.ones((batch_size,), dtype=torch.bool),  # start halted to force reset on first pass
+            current_data={k: torch.empty_like(v) for k, v in batch.items()}
+        )
+
+    def forward(self, carry: GLPS_ACTV1Carry, batch: Dict[str, torch.Tensor]):
+        # Reset halted seqs
+        new_inner_carry = self.inner.reset_carry(carry.halted, carry.inner_carry)
+        new_steps = torch.where(carry.halted, 0, carry.steps)
+        new_current_data = {k: torch.where(carry.halted.view((-1,) + (1,) * (batch[k].ndim - 1)), batch[k], v) for k, v in carry.current_data.items()}
+
+        # Inner step
+        new_inner_carry, logits, (q_halt_logits, q_continue_logits), conf = self.inner(new_inner_carry, new_current_data)
+
+        outputs = {
+            "logits": logits,
+            "q_halt_logits": q_halt_logits,
+            "q_continue_logits": q_continue_logits,
+            "conf": conf.squeeze(-1),
+        }
+
+        with torch.no_grad():
+            new_steps = new_steps + 1
+            is_last_step = new_steps >= self.config.halt_max_steps
+
+            # Combine halt signals: Q or confidence or last-step
+            halted = is_last_step | (q_halt_logits > q_continue_logits) | (conf.squeeze(-1) >= self.config.glps_tau_halt)
+
+            # Exploration during training only
+            if self.training and (self.config.halt_max_steps > 1):
+                min_halt_steps = (torch.rand_like(q_halt_logits) < self.config.halt_exploration_prob) * torch.randint_like(new_steps, low=2, high=self.config.halt_max_steps + 1)
+                halted = halted & (new_steps >= min_halt_steps)
+
+                # Optional target for Q-learning (kept similar to HRM)
+                next_conf = self.inner(new_inner_carry, new_current_data)[-1]
+                outputs["target_q_continue"] = torch.sigmoid(torch.where(is_last_step, q_halt_logits, torch.maximum(q_halt_logits, q_continue_logits)))
+
+        return GLPS_ACTV1Carry(new_inner_carry, new_steps, halted, new_current_data), outputs
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku/losses.py b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku/losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..cdfb77718bc16f98cdd7da06eb5795c36b866aa7
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku/losses.py
@@ -0,0 +1,102 @@
+from typing import Any, Tuple, Dict, Sequence, Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+import math
+
+IGNORE_LABEL_ID = -100
+
+
+def s(x, epsilon=1e-30):
+    return torch.where(
+        x<0,
+        1/(1-x+ epsilon),
+        x + 1
+    )
+
+
+def log_stablemax(x, dim=-1):
+    s_x = s(x)
+    return torch.log(s_x/torch.sum(s_x, dim=dim, keepdim=True))
+
+
+def stablemax_cross_entropy(logits, labels, ignore_index: int = -100, valid_mask=None):
+    logprobs = log_stablemax(logits.to(torch.float64), dim=-1)
+
+    if valid_mask is None:
+        valid_mask = (labels != ignore_index)
+    transformed_labels = torch.where(valid_mask, labels, 0)
+    prediction_logprobs = torch.gather(logprobs, index=transformed_labels.to(torch.long).unsqueeze(-1), dim=-1).squeeze(-1)
+
+    return -torch.where(valid_mask, prediction_logprobs, 0)
+
+
+def softmax_cross_entropy(logits, labels, ignore_index: int = -100):
+    # Cast logits to f32
+    # Flatten logits
+    return F.cross_entropy(logits.to(torch.float32).view(-1, logits.shape[-1]), labels.to(torch.long).view(-1), ignore_index=ignore_index, reduction="none").view(labels.shape)
+
+
+class ACTLossHead(nn.Module):
+    def __init__(self, model: nn.Module, loss_type: str):
+        super().__init__()
+        self.model = model
+        self.loss_fn = globals()[loss_type]
+        
+    def initial_carry(self, *args, **kwargs):
+        return self.model.initial_carry(*args, **kwargs)  # type: ignore
+
+    def forward(
+        self,
+        return_keys: Sequence[str],
+        # Model args
+        **model_kwargs,
+    ) -> Tuple[Any, torch.Tensor, Dict[str, torch.Tensor], Optional[Dict[str, torch.Tensor]], torch.Tensor]:
+        # Model logits
+        # B x SeqLen x D
+        new_carry, outputs = self.model(**model_kwargs)
+        labels = new_carry.current_data["labels"]
+
+        with torch.no_grad():
+            # Preds
+            outputs["preds"] = torch.argmax(outputs["logits"], dim=-1)
+
+            # Correctness
+            mask = (labels != IGNORE_LABEL_ID)
+            loss_counts = mask.sum(-1)
+            loss_divisor = loss_counts.clamp_min(1).unsqueeze(-1)  # Avoid NaNs in division
+
+            is_correct = mask & (torch.argmax(outputs["logits"], dim=-1) == labels)
+            seq_is_correct = is_correct.sum(-1) == loss_counts
+            
+            # Metrics (halted)
+            valid_metrics = new_carry.halted & (loss_counts > 0)
+            metrics = {
+                "count": valid_metrics.sum(),
+                
+                "accuracy":       torch.where(valid_metrics, (is_correct.to(torch.float32) / loss_divisor).sum(-1), 0).sum(),
+                "exact_accuracy": (valid_metrics & seq_is_correct).sum(),
+
+                "q_halt_accuracy": (valid_metrics & ((outputs["q_halt_logits"] >= 0) == seq_is_correct)).sum(),
+                "steps":          torch.where(valid_metrics, new_carry.steps, 0).sum(),
+            }
+
+        # Losses
+
+        lm_loss = (self.loss_fn(outputs["logits"], labels, ignore_index=IGNORE_LABEL_ID, valid_mask=mask) / loss_divisor).sum()
+        q_halt_loss = F.binary_cross_entropy_with_logits(outputs["q_halt_logits"], seq_is_correct.to(outputs["q_halt_logits"].dtype), reduction="sum")
+        metrics.update({
+            "lm_loss": lm_loss.detach(),
+            "q_halt_loss": q_halt_loss.detach(),
+        })
+        # Q continue (bootstrapping target loss); Alexia: This fits Q-learning, but seems totally unecessary
+        q_continue_loss = 0
+        if "target_q_continue" in outputs:
+            q_continue_loss = F.binary_cross_entropy_with_logits(outputs["q_continue_logits"], outputs["target_q_continue"], reduction="sum")
+
+            metrics["q_continue_loss"] = q_continue_loss.detach()
+        # Filter outputs for return
+        detached_outputs = {k: outputs[k].detach() for k in return_keys if k in outputs}
+
+        return new_carry, lm_loss + 0.5 * (q_halt_loss + q_continue_loss), metrics, detached_outputs, new_carry.halted.all()
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_nocompile/all_config.yaml b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_nocompile/all_config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..a88f4728aff6a19bd8a3c2b7169ed5d1120283de
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_nocompile/all_config.yaml
@@ -0,0 +1,56 @@
+arch:
+  H_cycles: 3
+  H_layers: 2
+  L_cycles: 1
+  L_layers: 4
+  dep_rank: 32
+  dep_topk: 8
+  expansion: 2.0
+  forward_dtype: bfloat16
+  glps_dep_graph: true
+  glps_enabled: true
+  glps_fill_obvious: true
+  glps_global_propagate_on_low_conf: true
+  glps_max_targeted_iters: 2
+  glps_tau_halt: 0.95
+  glps_tau_uncertain: 0.6
+  glps_token_masking: true
+  halt_exploration_prob: 0.1
+  halt_max_steps: 4
+  hidden_size: 512
+  loss:
+    loss_type: stablemax_cross_entropy
+    name: losses@ACTLossHead
+  mlp_t: false
+  name: recursive_reasoning.glps@GLPS_ACTV1
+  num_heads: 8
+  pos_encodings: rope
+  puzzle_emb_ndim: 0
+  rms_norm_eps: 1.0e-05
+  rope_theta: 10000.0
+beta1: 0.9
+beta2: 0.95
+checkpoint_every_eval: true
+checkpoint_path: checkpoints/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_nocompile
+data_paths:
+- data/sudoku-extreme-1k-aug-1000
+data_paths_test: []
+ema: true
+ema_rate: 0.999
+epochs: 50000
+eval_interval: 5000
+eval_save_outputs: []
+evaluators: []
+freeze_weights: false
+global_batch_size: 768
+load_checkpoint: null
+lr: 0.0001
+lr_min_ratio: 1.0
+lr_warmup_steps: 2000
+min_eval_interval: 0
+project_name: Sudoku-extreme-1k-aug-1000-ACT-torch
+puzzle_emb_lr: 0.0001
+puzzle_emb_weight_decay: 1.0
+run_name: pretrain_glps_sudoku_nocompile
+seed: 0
+weight_decay: 1.0
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_nocompile/glps.py b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_nocompile/glps.py
new file mode 100644
index 0000000000000000000000000000000000000000..6dc3a92d41e573ac49012f25f473b9ff02aaf42d
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_nocompile/glps.py
@@ -0,0 +1,406 @@
+
+from typing import Tuple, List, Dict
+from dataclasses import dataclass
+import math
+import torch
+import torch.nn.functional as F
+from torch import nn
+from pydantic import BaseModel
+
+# Reuse the same building blocks as HRM/TRM
+from models.common import trunc_normal_init_
+from models.layers import rms_norm, SwiGLU, Attention, RotaryEmbedding, CosSin, CastedEmbedding, CastedLinear
+from models.sparse_embedding import CastedSparseEmbedding
+
+"""
+Global-Local Predictive Solver (GLPS)
+------------------------------------
+A light-weight control-policy on top of the HRM/TRM style blocks:
+- H1: global scan -> certainty map
+- L1: fill-obvious (lock stable cells)
+- H2: dependency scoring over remaining cells
+- L2: targeted refinement (masked updates)
+- H3: energy-based confidence -> (optional) one global propagate sweep -> halt
+
+This file keeps parameter growth tiny: a few heads + (optional) low-rank dependency scorer.
+"""
+
+@dataclass
+class GLPS_ACTV1InnerCarry:
+    z_H: torch.Tensor
+    z_L: torch.Tensor
+
+@dataclass
+class GLPS_ACTV1Carry:
+    inner_carry: GLPS_ACTV1InnerCarry
+    steps: torch.Tensor
+    halted: torch.Tensor
+    current_data: Dict[str, torch.Tensor]
+
+class GLPS_ACTV1Config(BaseModel):
+    # Core IO / shapes
+    batch_size: int
+    seq_len: int
+    puzzle_emb_ndim: int = 0
+    num_puzzle_identifiers: int = 1
+    vocab_size: int = 256
+
+    # Cycle schedule
+    H_cycles: int = 3              # (scan -> refine -> check) typical
+    L_cycles: int = 1
+
+    # Depth
+    H_layers: int = 2
+    L_layers: int = 4
+
+    # Transformer config
+    hidden_size: int = 512
+    expansion: float = 2.0
+    num_heads: int = 8
+    pos_encodings: str = "rope"
+
+    rms_norm_eps: float = 1e-5
+    rope_theta: float = 10000.0
+
+    # ACT wrapper
+    halt_max_steps: int = 4
+    halt_exploration_prob: float = 0.1
+
+    forward_dtype: str = "bfloat16"
+
+    # Optional: use MLP on L instead of attention (matches HRM/TRM option)
+    mlp_t: bool = False
+
+    # ---- GLPS extras (tiny) ----
+    glps_enabled: bool = True
+    glps_fill_obvious: bool = True
+    glps_dep_graph: bool = True
+    glps_token_masking: bool = True
+    glps_global_propagate_on_low_conf: bool = True
+
+    glps_tau_halt: float = 0.95       # final confidence to halt
+    glps_tau_uncertain: float = 0.60  # cell-wise certainty threshold
+    glps_max_targeted_iters: int = 2  # small number: 1-2
+
+    # Dependency scorer (low rank bilinear)
+    dep_rank: int = 32
+    dep_topk: int = 8
+
+class GLPSBlock(nn.Module):
+    def __init__(self, config: GLPS_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        if self.config.mlp_t:
+            self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size)
+            self.mlp_t = SwiGLU(
+                hidden_size=self.config.seq_len + self.puzzle_emb_len, # treat sequence as channel
+                expansion=config.expansion,
+            )
+        else:
+            self.self_attn = Attention(
+                hidden_size=config.hidden_size,
+                head_dim=config.hidden_size // config.num_heads,
+                num_heads=config.num_heads,
+                num_key_value_heads=config.num_heads,
+                causal=False,
+            )
+        self.mlp = SwiGLU(hidden_size=config.hidden_size, expansion=config.expansion)
+        self.norm_eps = config.rms_norm_eps
+
+    def forward(self, cos_sin: CosSin, hidden_states: torch.Tensor) -> torch.Tensor:
+        if self.config.mlp_t:
+            # MLP over sequence dimension (mlp-t)
+            hidden_states = hidden_states.transpose(1, 2)
+            out = self.mlp_t(hidden_states)
+            hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+            hidden_states = hidden_states.transpose(1, 2)
+        else:
+            hidden_states = rms_norm(
+                hidden_states + self.self_attn(cos_sin=cos_sin, hidden_states=hidden_states),
+                variance_epsilon=self.norm_eps,
+            )
+        out = self.mlp(hidden_states)
+        hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+        return hidden_states
+
+class GLPSReasoningModule(nn.Module):
+    """Reasoning stack with optional masked updates (only update uncertain tokens)."""
+    def __init__(self, layers: List[GLPSBlock]):
+        super().__init__()
+        self.layers = torch.nn.ModuleList(layers)
+
+    def forward(self, hidden_states: torch.Tensor, input_injection: torch.Tensor, update_mask: torch.Tensor = None, **kwargs) -> torch.Tensor:
+        x = hidden_states
+        for layer in self.layers:
+            # Compute candidate update using injected context
+            y = layer(hidden_states=x + input_injection, **kwargs)
+            if update_mask is not None:
+                # Convex blend keeps frozen tokens unchanged
+                m = update_mask.to(x.dtype)[..., None]
+                x = x + m * (y - x)
+            else:
+                x = y
+        return x
+
+class GLPS_ACTV1_Inner(nn.Module):
+    def __init__(self, config: GLPS_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        self.forward_dtype = getattr(torch, self.config.forward_dtype)
+
+        # I/O
+        self.embed_scale  = math.sqrt(self.config.hidden_size)
+        embed_init_std = 1.0 / self.embed_scale
+
+        self.embed_tokens = CastedEmbedding(self.config.vocab_size, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+        self.lm_head      = CastedLinear(self.config.hidden_size, self.config.vocab_size, bias=False)
+        self.q_head       = CastedLinear(self.config.hidden_size, 2, bias=True)
+
+        # Puzzle emb (optional) — same convention as HRM/TRM
+        self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size)  # ceil div
+        if self.config.puzzle_emb_ndim > 0:
+            self.puzzle_emb = CastedSparseEmbedding(self.config.num_puzzle_identifiers, self.config.puzzle_emb_ndim, batch_size=self.config.batch_size, init_std=0, cast_to=self.forward_dtype)
+
+        # Positional encodings
+        if self.config.pos_encodings == "rope":
+            self.rotary_emb = RotaryEmbedding(dim=self.config.hidden_size // self.config.num_heads, max_position_embeddings=self.config.seq_len + self.puzzle_emb_len, base=self.config.rope_theta)
+        elif self.config.pos_encodings == "learned":
+            self.embed_pos = CastedEmbedding(self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+
+        # Reasoning stacks
+        self.H_level = GLPSReasoningModule(layers=[GLPSBlock(self.config) for _ in range(self.config.H_layers)])
+        self.L_level = GLPSReasoningModule(layers=[GLPSBlock(self.config) for _ in range(self.config.L_layers)])
+
+        # Initial states (match HRM/TRM style)
+        H_init = trunc_normal_init_(torch.empty(1, 1, self.config.hidden_size, dtype=self.forward_dtype), std=1.0)
+        L_init = trunc_normal_init_(torch.empty(1, 1, self.config.hidden_size, dtype=self.forward_dtype), std=1.0)
+        self.register_buffer("H_init", H_init, persistent=True)
+        self.register_buffer("L_init", L_init, persistent=True)
+
+        # GLPS small heads
+        self.candidate_head = CastedLinear(self.config.hidden_size, 16, bias=True)  # task-specific; for Sudoku you can slice to 9
+        self.certainty_head = CastedLinear(self.config.hidden_size, 1, bias=True)
+        self.energy_head    = CastedLinear(self.config.hidden_size, 1, bias=True)
+
+        # Low-rank dependency scorer (shared)
+        r = max(1, self.config.dep_rank)
+        self.dep_q = CastedLinear(self.config.hidden_size, r, bias=False)
+        self.dep_k = CastedLinear(self.config.hidden_size, r, bias=False)
+
+        # Q head init like HRM/TRM (near-zero -> easier bootstrapping)
+        with torch.no_grad():
+            self.q_head.weight.zero_()
+            self.q_head.bias.fill_(-5)
+
+    def _input_embeddings(self, input: torch.Tensor, puzzle_identifiers: torch.Tensor):
+        # Token embedding
+        embedding = self.embed_tokens(input.to(torch.int32))
+
+        # Puzzle embeddings
+        if self.config.puzzle_emb_ndim > 0:
+            puzzle_embedding = self.puzzle_emb(puzzle_identifiers)
+            pad_count = self.puzzle_emb_len * self.config.hidden_size - puzzle_embedding.shape[-1]
+            if pad_count > 0:
+                puzzle_embedding = F.pad(puzzle_embedding, (0, pad_count))
+            embedding = torch.cat((puzzle_embedding.view(-1, self.puzzle_emb_len, self.config.hidden_size), embedding), dim=-2)
+
+        # Position embeddings
+        if self.config.pos_encodings == "learned":
+            embedding = 0.707106781 * (embedding + self.embed_pos.embedding_weight.to(self.forward_dtype))
+
+        return self.embed_scale * embedding
+
+    def empty_carry(self, batch_size: int):
+        return GLPS_ACTV1InnerCarry(
+            z_H=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+            z_L=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+        )
+
+    def reset_carry(self, reset_flag: torch.Tensor, carry: GLPS_ACTV1InnerCarry):
+        # Explicitly expand buffers and mask to target shapes to avoid shape confusion
+        B, L, D = carry.z_H.shape
+        # Reduce/reset flag to per-batch boolean vector of shape [B]
+        if reset_flag.ndim == 1 and reset_flag.shape[0] == B:
+            reset_b = reset_flag.to(torch.bool)
+        else:
+            # If shape is [B, ...] reduce across non-batch dims; otherwise fallback to first B entries
+            try:
+                reset_b = reset_flag.reshape(B, -1).any(dim=1).to(torch.bool)
+            except Exception:
+                reset_b = reset_flag.reshape(-1)[:B].to(torch.bool)
+        m = reset_b.view(B, 1, 1)
+        mH = m.expand(B, L, D)
+        mL = mH  # same shape for z_L
+        H_init_exp = self.H_init.expand(B, L, D)
+        L_init_exp = self.L_init.expand(B, L, D)
+        return GLPS_ACTV1InnerCarry(
+            z_H=torch.where(mH, H_init_exp, carry.z_H),
+            z_L=torch.where(mL, L_init_exp, carry.z_L),
+        )
+
+    def _global_scan(self, z_L, z_H, input_embeddings, seq_info):
+        # One light pass to gather global signals
+        z_scan = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+        cand_logits = self.candidate_head(z_scan)              # [B, L, C]
+        certainty   = torch.sigmoid(self.certainty_head(z_scan))  # [B, L, 1]
+        return z_scan, cand_logits, certainty
+
+    def _build_dep_mask(self, z_ctx, uncertain_mask: torch.Tensor):
+        """Compute a dependency-based focus mask from a low-rank bilinear score.
+        uncertain_mask: [B, L] boolean mask of cells that are currently uncertain
+        Returns: dep_mask [B, L] boolean mask of cells to (re)update.
+        """
+        B, L, D = z_ctx.shape
+        # Project to low-rank space; use float32 to avoid dtype mismatches under torch.compile
+        Q = self.dep_q(z_ctx).to(torch.float32)   # [B, L, r]
+        K = self.dep_k(z_ctx).to(torch.float32)   # [B, L, r]
+        r = max(1, int(Q.shape[-1]))
+        sim = torch.matmul(Q, K.transpose(1, 2))  # [B, L, L] (float32)
+        sim = sim / math.sqrt(r)
+
+        # Aggregate influence from uncertain queries onto target tokens
+        src = uncertain_mask.to(sim.dtype).unsqueeze(1)      # [B, 1, L]
+        influence = torch.matmul(src, sim).squeeze(1)        # [B, L]
+
+        # Top-k influenced tokens per batch
+        topk = min(self.config.dep_topk, L)
+        vals, idx = torch.topk(influence, k=topk, dim=-1)    # [B, topk]
+        dep_mask = torch.zeros_like(uncertain_mask)
+        dep_mask.scatter_(1, idx, True)
+
+        # Always include uncertain cells themselves
+        dep_mask = dep_mask | uncertain_mask
+        return dep_mask
+
+    def forward(self, carry: GLPS_ACTV1InnerCarry, batch: Dict[str, torch.Tensor]):
+        seq_info = dict(cos_sin=getattr(self, "rotary_emb", None)() if hasattr(self, "rotary_emb") else None)
+
+        # Encode inputs
+        input_embeddings = self._input_embeddings(batch["inputs"], batch["puzzle_identifiers"])
+
+        # States
+        z_H, z_L = carry.z_H, carry.z_L
+
+        if not self.config.glps_enabled:
+            # Fallback to an HRM-like single-cycle grad update for compatibility
+            with torch.no_grad():
+                for _H in range(self.config.H_cycles - 1):
+                    for _L in range(self.config.L_cycles):
+                        z_L = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+                    z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+            # final grad step
+            for _L in range(self.config.L_cycles):
+                z_L = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+            z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+
+            # Outputs
+            new_carry = GLPS_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach())
+            logits = self.lm_head(z_H)[:, self.puzzle_emb_len:]
+            q_logits = self.q_head(z_H[:, 0]).to(torch.float32)
+            conf = torch.zeros_like(q_logits[..., :1]) + 0.5  # neutral
+            return new_carry, logits, (q_logits[..., 0], q_logits[..., 1]), conf
+
+        # ===== GLPS path =====
+        # H1: global scan (cheap)
+        with torch.no_grad():
+            z_scan, cand_logits, certainty = self._global_scan(z_L, z_H, input_embeddings, seq_info)
+
+        # L1: fill-obvious -> compute stable vs uncertain masks
+        if self.config.glps_fill_obvious:
+            obvious_mask = (certainty >= self.config.glps_tau_uncertain)  # [B, L, 1]
+        else:
+            obvious_mask = torch.zeros_like(certainty).bool()
+        stable_mask = obvious_mask.squeeze(-1)         # [B, L]
+        uncertain_mask = ~stable_mask                  # [B, L]
+
+        # H2: dependency prediction over remaining cells
+        if self.config.glps_dep_graph:
+            dep_mask = self._build_dep_mask(z_scan, uncertain_mask)   # [B, L]
+        else:
+            dep_mask = uncertain_mask
+
+        # L2: targeted refinement (a couple of masked iters)
+        update_mask = dep_mask if self.config.glps_token_masking else None
+        z = z_scan.detach()  # use scanned context as start
+        for _ in range(self.config.glps_max_targeted_iters):
+            z = self.L_level(z, z_H + input_embeddings, update_mask=update_mask, **seq_info)
+            # Refresh certainty to shrink mask (optional but cheap)
+            cert_now = torch.sigmoid(self.certainty_head(z)).squeeze(-1)
+            if self.config.glps_token_masking:
+                update_mask = dep_mask & (cert_now < self.config.glps_tau_uncertain)
+
+        # Merge into H and do a light H update with grad
+        z_L = z
+        z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+
+        # H3: energy/consistency -> confidence & optional global propagate
+        energy = torch.sigmoid(self.energy_head(z_H)).mean(dim=1)  # [B, 1]
+        conf = 1.0 - energy
+        need_sweep = (conf.squeeze(-1) < self.config.glps_tau_halt)
+        if self.config.glps_global_propagate_on_low_conf and need_sweep.any():
+            # one final full sweep only for rows needing it
+            maskB = need_sweep.view(-1, 1, 1)
+            zL2 = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+            zH2 = self.H_level(z_H, zL2, update_mask=None, **seq_info)
+            z_L = torch.where(maskB, zL2, z_L)
+            z_H = torch.where(maskB, zH2, z_H)
+
+        # Outputs
+        new_carry = GLPS_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach())
+        logits = self.lm_head(z_H)[:, self.puzzle_emb_len:]
+        q_logits = self.q_head(z_H[:, 0]).to(torch.float32)
+        return new_carry, logits, (q_logits[..., 0], q_logits[..., 1]), conf
+
+class GLPS_ACTV1(nn.Module):
+    """ACT-style wrapper that mixes Q-halt with GLPS confidence."""
+    def __init__(self, config_dict: dict):
+        super().__init__()
+        self.config = GLPS_ACTV1Config(**config_dict)
+        self.inner = GLPS_ACTV1_Inner(self.config)
+
+    @property
+    def puzzle_emb(self):
+        return self.inner.puzzle_emb
+
+    def initial_carry(self, batch: Dict[str, torch.Tensor]):
+        batch_size = batch["inputs"].shape[0]
+        return GLPS_ACTV1Carry(
+            inner_carry=self.inner.empty_carry(batch_size),
+            steps=torch.zeros((batch_size,), dtype=torch.int32),
+            halted=torch.ones((batch_size,), dtype=torch.bool),  # start halted to force reset on first pass
+            current_data={k: torch.empty_like(v) for k, v in batch.items()}
+        )
+
+    def forward(self, carry: GLPS_ACTV1Carry, batch: Dict[str, torch.Tensor]):
+        # Reset halted seqs
+        new_inner_carry = self.inner.reset_carry(carry.halted, carry.inner_carry)
+        new_steps = torch.where(carry.halted, 0, carry.steps)
+        new_current_data = {k: torch.where(carry.halted.view((-1,) + (1,) * (batch[k].ndim - 1)), batch[k], v) for k, v in carry.current_data.items()}
+
+        # Inner step
+        new_inner_carry, logits, (q_halt_logits, q_continue_logits), conf = self.inner(new_inner_carry, new_current_data)
+
+        outputs = {
+            "logits": logits,
+            "q_halt_logits": q_halt_logits,
+            "q_continue_logits": q_continue_logits,
+            "conf": conf.squeeze(-1),
+        }
+
+        with torch.no_grad():
+            new_steps = new_steps + 1
+            is_last_step = new_steps >= self.config.halt_max_steps
+
+            # Combine halt signals: Q or confidence or last-step
+            halted = is_last_step | (q_halt_logits > q_continue_logits) | (conf.squeeze(-1) >= self.config.glps_tau_halt)
+
+            # Exploration during training only
+            if self.training and (self.config.halt_max_steps > 1):
+                min_halt_steps = (torch.rand_like(q_halt_logits) < self.config.halt_exploration_prob) * torch.randint_like(new_steps, low=2, high=self.config.halt_max_steps + 1)
+                halted = halted & (new_steps >= min_halt_steps)
+
+                # Optional target for Q-learning (kept similar to HRM)
+                next_conf = self.inner(new_inner_carry, new_current_data)[-1]
+                outputs["target_q_continue"] = torch.sigmoid(torch.where(is_last_step, q_halt_logits, torch.maximum(q_halt_logits, q_continue_logits)))
+
+        return GLPS_ACTV1Carry(new_inner_carry, new_steps, halted, new_current_data), outputs
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_nocompile/losses.py b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_nocompile/losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..cdfb77718bc16f98cdd7da06eb5795c36b866aa7
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_nocompile/losses.py
@@ -0,0 +1,102 @@
+from typing import Any, Tuple, Dict, Sequence, Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+import math
+
+IGNORE_LABEL_ID = -100
+
+
+def s(x, epsilon=1e-30):
+    return torch.where(
+        x<0,
+        1/(1-x+ epsilon),
+        x + 1
+    )
+
+
+def log_stablemax(x, dim=-1):
+    s_x = s(x)
+    return torch.log(s_x/torch.sum(s_x, dim=dim, keepdim=True))
+
+
+def stablemax_cross_entropy(logits, labels, ignore_index: int = -100, valid_mask=None):
+    logprobs = log_stablemax(logits.to(torch.float64), dim=-1)
+
+    if valid_mask is None:
+        valid_mask = (labels != ignore_index)
+    transformed_labels = torch.where(valid_mask, labels, 0)
+    prediction_logprobs = torch.gather(logprobs, index=transformed_labels.to(torch.long).unsqueeze(-1), dim=-1).squeeze(-1)
+
+    return -torch.where(valid_mask, prediction_logprobs, 0)
+
+
+def softmax_cross_entropy(logits, labels, ignore_index: int = -100):
+    # Cast logits to f32
+    # Flatten logits
+    return F.cross_entropy(logits.to(torch.float32).view(-1, logits.shape[-1]), labels.to(torch.long).view(-1), ignore_index=ignore_index, reduction="none").view(labels.shape)
+
+
+class ACTLossHead(nn.Module):
+    def __init__(self, model: nn.Module, loss_type: str):
+        super().__init__()
+        self.model = model
+        self.loss_fn = globals()[loss_type]
+        
+    def initial_carry(self, *args, **kwargs):
+        return self.model.initial_carry(*args, **kwargs)  # type: ignore
+
+    def forward(
+        self,
+        return_keys: Sequence[str],
+        # Model args
+        **model_kwargs,
+    ) -> Tuple[Any, torch.Tensor, Dict[str, torch.Tensor], Optional[Dict[str, torch.Tensor]], torch.Tensor]:
+        # Model logits
+        # B x SeqLen x D
+        new_carry, outputs = self.model(**model_kwargs)
+        labels = new_carry.current_data["labels"]
+
+        with torch.no_grad():
+            # Preds
+            outputs["preds"] = torch.argmax(outputs["logits"], dim=-1)
+
+            # Correctness
+            mask = (labels != IGNORE_LABEL_ID)
+            loss_counts = mask.sum(-1)
+            loss_divisor = loss_counts.clamp_min(1).unsqueeze(-1)  # Avoid NaNs in division
+
+            is_correct = mask & (torch.argmax(outputs["logits"], dim=-1) == labels)
+            seq_is_correct = is_correct.sum(-1) == loss_counts
+            
+            # Metrics (halted)
+            valid_metrics = new_carry.halted & (loss_counts > 0)
+            metrics = {
+                "count": valid_metrics.sum(),
+                
+                "accuracy":       torch.where(valid_metrics, (is_correct.to(torch.float32) / loss_divisor).sum(-1), 0).sum(),
+                "exact_accuracy": (valid_metrics & seq_is_correct).sum(),
+
+                "q_halt_accuracy": (valid_metrics & ((outputs["q_halt_logits"] >= 0) == seq_is_correct)).sum(),
+                "steps":          torch.where(valid_metrics, new_carry.steps, 0).sum(),
+            }
+
+        # Losses
+
+        lm_loss = (self.loss_fn(outputs["logits"], labels, ignore_index=IGNORE_LABEL_ID, valid_mask=mask) / loss_divisor).sum()
+        q_halt_loss = F.binary_cross_entropy_with_logits(outputs["q_halt_logits"], seq_is_correct.to(outputs["q_halt_logits"].dtype), reduction="sum")
+        metrics.update({
+            "lm_loss": lm_loss.detach(),
+            "q_halt_loss": q_halt_loss.detach(),
+        })
+        # Q continue (bootstrapping target loss); Alexia: This fits Q-learning, but seems totally unecessary
+        q_continue_loss = 0
+        if "target_q_continue" in outputs:
+            q_continue_loss = F.binary_cross_entropy_with_logits(outputs["q_continue_logits"], outputs["target_q_continue"], reduction="sum")
+
+            metrics["q_continue_loss"] = q_continue_loss.detach()
+        # Filter outputs for return
+        detached_outputs = {k: outputs[k].detach() for k in return_keys if k in outputs}
+
+        return new_carry, lm_loss + 0.5 * (q_halt_loss + q_continue_loss), metrics, detached_outputs, new_carry.halted.all()
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v2/all_config.yaml b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v2/all_config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..602747541cc0a7c2dc817cdbf4fd34ccbb80804e
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v2/all_config.yaml
@@ -0,0 +1,56 @@
+arch:
+  H_cycles: 3
+  H_layers: 2
+  L_cycles: 1
+  L_layers: 6
+  dep_rank: 64
+  dep_topk: 10
+  expansion: 4.0
+  forward_dtype: bfloat16
+  glps_dep_graph: true
+  glps_enabled: true
+  glps_fill_obvious: true
+  glps_global_propagate_on_low_conf: true
+  glps_max_targeted_iters: 3
+  glps_tau_halt: 0.95
+  glps_tau_uncertain: 0.8
+  glps_token_masking: true
+  halt_exploration_prob: 0.1
+  halt_max_steps: 16
+  hidden_size: 512
+  loss:
+    loss_type: stablemax_cross_entropy
+    name: losses@ACTLossHead
+  mlp_t: false
+  name: recursive_reasoning.glps@GLPS_ACTV1
+  num_heads: 8
+  pos_encodings: rope
+  puzzle_emb_ndim: 0
+  rms_norm_eps: 1.0e-05
+  rope_theta: 10000.0
+beta1: 0.9
+beta2: 0.95
+checkpoint_every_eval: true
+checkpoint_path: checkpoints/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v2
+data_paths:
+- data/sudoku-extreme-1k-aug-1000
+data_paths_test: []
+ema: true
+ema_rate: 0.999
+epochs: 50000
+eval_interval: 5000
+eval_save_outputs: []
+evaluators: []
+freeze_weights: false
+global_batch_size: 768
+load_checkpoint: null
+lr: 0.0001
+lr_min_ratio: 1.0
+lr_warmup_steps: 2000
+min_eval_interval: 0
+project_name: Sudoku-extreme-1k-aug-1000-ACT-torch
+puzzle_emb_lr: 0.0001
+puzzle_emb_weight_decay: 1.0
+run_name: pretrain_glps_sudoku_v2
+seed: 0
+weight_decay: 1.0
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v2/glps.py b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v2/glps.py
new file mode 100644
index 0000000000000000000000000000000000000000..96e6de932e79e174cf773779293a643842e8a268
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v2/glps.py
@@ -0,0 +1,409 @@
+
+from typing import Tuple, List, Dict
+from dataclasses import dataclass
+import math
+import torch
+import torch.nn.functional as F
+from torch import nn
+from pydantic import BaseModel
+
+# Reuse the same building blocks as HRM/TRM
+from models.common import trunc_normal_init_
+from models.layers import rms_norm, SwiGLU, Attention, RotaryEmbedding, CosSin, CastedEmbedding, CastedLinear
+from models.sparse_embedding import CastedSparseEmbedding
+
+"""
+Global-Local Predictive Solver (GLPS)
+------------------------------------
+A light-weight control-policy on top of the HRM/TRM style blocks:
+- H1: global scan -> certainty map
+- L1: fill-obvious (lock stable cells)
+- H2: dependency scoring over remaining cells
+- L2: targeted refinement (masked updates)
+- H3: energy-based confidence -> (optional) one global propagate sweep -> halt
+
+This file keeps parameter growth tiny: a few heads + (optional) low-rank dependency scorer.
+"""
+
+@dataclass
+class GLPS_ACTV1InnerCarry:
+    z_H: torch.Tensor
+    z_L: torch.Tensor
+
+@dataclass
+class GLPS_ACTV1Carry:
+    inner_carry: GLPS_ACTV1InnerCarry
+    steps: torch.Tensor
+    halted: torch.Tensor
+    current_data: Dict[str, torch.Tensor]
+
+class GLPS_ACTV1Config(BaseModel):
+    # Core IO / shapes
+    batch_size: int
+    seq_len: int
+    puzzle_emb_ndim: int = 0
+    num_puzzle_identifiers: int = 1
+    vocab_size: int = 256
+
+    # Cycle schedule
+    H_cycles: int = 3              # (scan -> refine -> check) typical
+    L_cycles: int = 1
+
+    # Depth
+    H_layers: int = 2
+    L_layers: int = 4
+
+    # Transformer config
+    hidden_size: int = 512
+    expansion: float = 2.0
+    num_heads: int = 8
+    pos_encodings: str = "rope"
+
+    rms_norm_eps: float = 1e-5
+    rope_theta: float = 10000.0
+
+    # ACT wrapper
+    halt_max_steps: int = 4
+    halt_exploration_prob: float = 0.1
+
+    forward_dtype: str = "bfloat16"
+
+    # Optional: use MLP on L instead of attention (matches HRM/TRM option)
+    mlp_t: bool = False
+
+    # ---- GLPS extras (tiny) ----
+    glps_enabled: bool = True
+    glps_fill_obvious: bool = True
+    glps_dep_graph: bool = True
+    glps_token_masking: bool = True
+    glps_global_propagate_on_low_conf: bool = True
+
+    glps_tau_halt: float = 0.95       # final confidence to halt
+    glps_tau_uncertain: float = 0.60  # cell-wise certainty threshold
+    glps_max_targeted_iters: int = 2  # small number: 1-2
+
+    # Dependency scorer (low rank bilinear)
+    dep_rank: int = 32
+    dep_topk: int = 8
+
+class GLPSBlock(nn.Module):
+    def __init__(self, config: GLPS_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        if self.config.mlp_t:
+            self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size)
+            self.mlp_t = SwiGLU(
+                hidden_size=self.config.seq_len + self.puzzle_emb_len, # treat sequence as channel
+                expansion=config.expansion,
+            )
+        else:
+            self.self_attn = Attention(
+                hidden_size=config.hidden_size,
+                head_dim=config.hidden_size // config.num_heads,
+                num_heads=config.num_heads,
+                num_key_value_heads=config.num_heads,
+                causal=False,
+            )
+        self.mlp = SwiGLU(hidden_size=config.hidden_size, expansion=config.expansion)
+        self.norm_eps = config.rms_norm_eps
+
+    def forward(self, cos_sin: CosSin, hidden_states: torch.Tensor) -> torch.Tensor:
+        if self.config.mlp_t:
+            # MLP over sequence dimension (mlp-t)
+            hidden_states = hidden_states.transpose(1, 2)
+            out = self.mlp_t(hidden_states)
+            hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+            hidden_states = hidden_states.transpose(1, 2)
+        else:
+            hidden_states = rms_norm(
+                hidden_states + self.self_attn(cos_sin=cos_sin, hidden_states=hidden_states),
+                variance_epsilon=self.norm_eps,
+            )
+        out = self.mlp(hidden_states)
+        hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+        return hidden_states
+
+class GLPSReasoningModule(nn.Module):
+    """Reasoning stack with optional masked updates (only update uncertain tokens)."""
+    def __init__(self, layers: List[GLPSBlock]):
+        super().__init__()
+        self.layers = torch.nn.ModuleList(layers)
+
+    def forward(self, hidden_states: torch.Tensor, input_injection: torch.Tensor, update_mask: torch.Tensor = None, **kwargs) -> torch.Tensor:
+        x = hidden_states
+        for layer in self.layers:
+            # Compute candidate update using injected context
+            y = layer(hidden_states=x + input_injection, **kwargs)
+            if update_mask is not None:
+                # Convex blend keeps frozen tokens unchanged
+                m = update_mask.to(x.dtype)[..., None]
+                x = x + m * (y - x)
+            else:
+                x = y
+        return x
+
+class GLPS_ACTV1_Inner(nn.Module):
+    def __init__(self, config: GLPS_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        self.forward_dtype = getattr(torch, self.config.forward_dtype)
+
+        # I/O
+        self.embed_scale  = math.sqrt(self.config.hidden_size)
+        embed_init_std = 1.0 / self.embed_scale
+
+        self.embed_tokens = CastedEmbedding(self.config.vocab_size, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+        self.lm_head      = CastedLinear(self.config.hidden_size, self.config.vocab_size, bias=False)
+        self.q_head       = CastedLinear(self.config.hidden_size, 2, bias=True)
+
+        # Puzzle emb (optional) — same convention as HRM/TRM
+        self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size)  # ceil div
+        if self.config.puzzle_emb_ndim > 0:
+            self.puzzle_emb = CastedSparseEmbedding(self.config.num_puzzle_identifiers, self.config.puzzle_emb_ndim, batch_size=self.config.batch_size, init_std=0, cast_to=self.forward_dtype)
+
+        # Positional encodings
+        if self.config.pos_encodings == "rope":
+            self.rotary_emb = RotaryEmbedding(dim=self.config.hidden_size // self.config.num_heads, max_position_embeddings=self.config.seq_len + self.puzzle_emb_len, base=self.config.rope_theta)
+        elif self.config.pos_encodings == "learned":
+            self.embed_pos = CastedEmbedding(self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+
+        # Reasoning stacks
+        self.H_level = GLPSReasoningModule(layers=[GLPSBlock(self.config) for _ in range(self.config.H_layers)])
+        self.L_level = GLPSReasoningModule(layers=[GLPSBlock(self.config) for _ in range(self.config.L_layers)])
+
+        # Initial states (match HRM/TRM style)
+        H_init = trunc_normal_init_(torch.empty(1, 1, self.config.hidden_size, dtype=self.forward_dtype), std=1.0)
+        L_init = trunc_normal_init_(torch.empty(1, 1, self.config.hidden_size, dtype=self.forward_dtype), std=1.0)
+        self.register_buffer("H_init", H_init, persistent=True)
+        self.register_buffer("L_init", L_init, persistent=True)
+
+        # GLPS small heads
+        self.candidate_head = CastedLinear(self.config.hidden_size, 16, bias=True)  # task-specific; for Sudoku you can slice to 9
+        self.certainty_head = CastedLinear(self.config.hidden_size, 1, bias=True)
+        self.energy_head    = CastedLinear(self.config.hidden_size, 1, bias=True)
+
+        # Low-rank dependency scorer (shared)
+        r = max(1, self.config.dep_rank)
+        self.dep_q = CastedLinear(self.config.hidden_size, r, bias=False)
+        self.dep_k = CastedLinear(self.config.hidden_size, r, bias=False)
+
+        # Q head init like HRM/TRM (near-zero -> easier bootstrapping)
+        with torch.no_grad():
+            self.q_head.weight.zero_()
+            self.q_head.bias.fill_(-5)
+
+    def _input_embeddings(self, input: torch.Tensor, puzzle_identifiers: torch.Tensor):
+        # Token embedding
+        embedding = self.embed_tokens(input.to(torch.int32))
+
+        # Puzzle embeddings
+        if self.config.puzzle_emb_ndim > 0:
+            puzzle_embedding = self.puzzle_emb(puzzle_identifiers)
+            pad_count = self.puzzle_emb_len * self.config.hidden_size - puzzle_embedding.shape[-1]
+            if pad_count > 0:
+                puzzle_embedding = F.pad(puzzle_embedding, (0, pad_count))
+            embedding = torch.cat((puzzle_embedding.view(-1, self.puzzle_emb_len, self.config.hidden_size), embedding), dim=-2)
+
+        # Position embeddings
+        if self.config.pos_encodings == "learned":
+            embedding = 0.707106781 * (embedding + self.embed_pos.embedding_weight.to(self.forward_dtype))
+
+        return self.embed_scale * embedding
+
+    def empty_carry(self, batch_size: int):
+        return GLPS_ACTV1InnerCarry(
+            z_H=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+            z_L=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+        )
+
+    def reset_carry(self, reset_flag: torch.Tensor, carry: GLPS_ACTV1InnerCarry):
+        # Explicitly expand buffers and mask to target shapes to avoid shape confusion
+        B, L, D = carry.z_H.shape
+        # Reduce/reset flag to per-batch boolean vector of shape [B]
+        if reset_flag.ndim == 1 and reset_flag.shape[0] == B:
+            reset_b = reset_flag.to(torch.bool)
+        else:
+            # If shape is [B, ...] reduce across non-batch dims; otherwise fallback to first B entries
+            try:
+                reset_b = reset_flag.reshape(B, -1).any(dim=1).to(torch.bool)
+            except Exception:
+                reset_b = reset_flag.reshape(-1)[:B].to(torch.bool)
+        m = reset_b.view(B, 1, 1)
+        mH = m.expand(B, L, D)
+        mL = mH  # same shape for z_L
+        H_init_exp = self.H_init.expand(B, L, D)
+        L_init_exp = self.L_init.expand(B, L, D)
+        return GLPS_ACTV1InnerCarry(
+            z_H=torch.where(mH, H_init_exp, carry.z_H),
+            z_L=torch.where(mL, L_init_exp, carry.z_L),
+        )
+
+    def _global_scan(self, z_L, z_H, input_embeddings, seq_info):
+        # One light pass to gather global signals
+        z_scan = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+        cand_logits = self.candidate_head(z_scan)              # [B, L, C]
+        certainty   = torch.sigmoid(self.certainty_head(z_scan))  # [B, L, 1]
+        return z_scan, cand_logits, certainty
+
+    def _build_dep_mask(self, z_ctx, uncertain_mask: torch.Tensor):
+        """Compute a dependency-based focus mask from a low-rank bilinear score.
+        uncertain_mask: [B, L] boolean mask of cells that are currently uncertain
+        Returns: dep_mask [B, L] boolean mask of cells to (re)update.
+        """
+        B, L, D = z_ctx.shape
+        # Project to low-rank space; use float32 to avoid dtype mismatches under torch.compile
+        Q = self.dep_q(z_ctx).to(torch.float32)   # [B, L, r]
+        K = self.dep_k(z_ctx).to(torch.float32)   # [B, L, r]
+        r = max(1, int(Q.shape[-1]))
+        sim = torch.matmul(Q, K.transpose(1, 2))  # [B, L, L] (float32)
+        sim = sim / math.sqrt(r)
+
+        # Aggregate influence from uncertain queries onto target tokens
+        src = uncertain_mask.to(sim.dtype).unsqueeze(1)      # [B, 1, L]
+        influence = torch.matmul(src, sim).squeeze(1)        # [B, L]
+
+        # Top-k influenced tokens per batch
+        topk = min(self.config.dep_topk, L)
+        vals, idx = torch.topk(influence, k=topk, dim=-1)    # [B, topk]
+        dep_mask = torch.zeros_like(uncertain_mask)
+        dep_mask.scatter_(1, idx, True)
+
+        # Always include uncertain cells themselves
+        dep_mask = dep_mask | uncertain_mask
+        return dep_mask
+
+    def forward(self, carry: GLPS_ACTV1InnerCarry, batch: Dict[str, torch.Tensor]):
+        seq_info = dict(cos_sin=getattr(self, "rotary_emb", None)() if hasattr(self, "rotary_emb") else None)
+
+        # Encode inputs
+        input_embeddings = self._input_embeddings(batch["inputs"], batch["puzzle_identifiers"])
+
+        # States
+        z_H, z_L = carry.z_H, carry.z_L
+
+        if not self.config.glps_enabled:
+            # Fallback to an HRM-like single-cycle grad update for compatibility
+            with torch.no_grad():
+                for _H in range(self.config.H_cycles - 1):
+                    for _L in range(self.config.L_cycles):
+                        z_L = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+                    z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+            # final grad step
+            for _L in range(self.config.L_cycles):
+                z_L = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+            z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+
+            # Outputs
+            new_carry = GLPS_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach())
+            logits = self.lm_head(z_H)[:, self.puzzle_emb_len:]
+            q_logits = self.q_head(z_H[:, 0]).to(torch.float32)
+            conf = torch.zeros_like(q_logits[..., :1]) + 0.5  # neutral
+            return new_carry, logits, (q_logits[..., 0], q_logits[..., 1]), conf
+
+        # ===== GLPS path =====
+        # H1: global scan (cheap)
+        with torch.no_grad():
+            z_scan, cand_logits, certainty = self._global_scan(z_L, z_H, input_embeddings, seq_info)
+
+        # L1: fill-obvious -> compute stable vs uncertain masks
+        if self.config.glps_fill_obvious:
+            obvious_mask = (certainty >= self.config.glps_tau_uncertain)  # [B, L, 1]
+        else:
+            obvious_mask = torch.zeros_like(certainty).bool()
+        stable_mask = obvious_mask.squeeze(-1)         # [B, L]
+        uncertain_mask = ~stable_mask                  # [B, L]
+
+        # H2: dependency prediction over remaining cells
+        if self.config.glps_dep_graph:
+            dep_mask = self._build_dep_mask(z_scan, uncertain_mask)   # [B, L]
+        else:
+            dep_mask = uncertain_mask
+
+        # L2: targeted refinement (a couple of masked iters)
+        update_mask = dep_mask if self.config.glps_token_masking else None
+        z = z_scan.detach()  # use scanned context as start
+        for _ in range(self.config.glps_max_targeted_iters):
+            z = self.L_level(z, z_H + input_embeddings, update_mask=update_mask, **seq_info)
+            # Refresh certainty to shrink mask (optional but cheap)
+            cert_now = torch.sigmoid(self.certainty_head(z)).squeeze(-1)
+            if self.config.glps_token_masking:
+                update_mask = dep_mask & (cert_now < self.config.glps_tau_uncertain)
+
+        # Merge into H and do a light H update with grad
+        z_L = z
+        z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+
+        # H3: energy/consistency -> confidence & optional global propagate
+        energy = torch.sigmoid(self.energy_head(z_H)).mean(dim=1)  # [B, 1]
+        conf = 1.0 - energy
+        need_sweep = (conf.squeeze(-1) < self.config.glps_tau_halt)
+        if self.config.glps_global_propagate_on_low_conf and need_sweep.any():
+            # one final full sweep only for rows needing it
+            maskB = need_sweep.view(-1, 1, 1)
+            zL2 = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+            zH2 = self.H_level(z_H, zL2, update_mask=None, **seq_info)
+            z_L = torch.where(maskB, zL2, z_L)
+            z_H = torch.where(maskB, zH2, z_H)
+
+        # Outputs
+        new_carry = GLPS_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach())
+        logits = self.lm_head(z_H)[:, self.puzzle_emb_len:]
+        q_logits = self.q_head(z_H[:, 0]).to(torch.float32)
+        return new_carry, logits, (q_logits[..., 0], q_logits[..., 1]), conf
+
+class GLPS_ACTV1(nn.Module):
+    """ACT-style wrapper that mixes Q-halt with GLPS confidence."""
+    def __init__(self, config_dict: dict):
+        super().__init__()
+        self.config = GLPS_ACTV1Config(**config_dict)
+        self.inner = GLPS_ACTV1_Inner(self.config)
+
+    @property
+    def puzzle_emb(self):
+        return self.inner.puzzle_emb
+
+    def initial_carry(self, batch: Dict[str, torch.Tensor]):
+        batch_size = batch["inputs"].shape[0]
+        return GLPS_ACTV1Carry(
+            inner_carry=self.inner.empty_carry(batch_size),
+            steps=torch.zeros((batch_size,), dtype=torch.int32),
+            halted=torch.ones((batch_size,), dtype=torch.bool),  # start halted to force reset on first pass
+            current_data={k: torch.empty_like(v) for k, v in batch.items()}
+        )
+
+    def forward(self, carry: GLPS_ACTV1Carry, batch: Dict[str, torch.Tensor]):
+        # Reset halted seqs
+        new_inner_carry = self.inner.reset_carry(carry.halted, carry.inner_carry)
+        new_steps = torch.where(carry.halted, 0, carry.steps)
+        new_current_data = {k: torch.where(carry.halted.view((-1,) + (1,) * (batch[k].ndim - 1)), batch[k], v) for k, v in carry.current_data.items()}
+
+        # Inner step
+        new_inner_carry, logits, (q_halt_logits, q_continue_logits), conf = self.inner(new_inner_carry, new_current_data)
+
+        outputs = {
+            "logits": logits,
+            "q_halt_logits": q_halt_logits,
+            "q_continue_logits": q_continue_logits,
+            "conf": conf.squeeze(-1),
+        }
+
+        with torch.no_grad():
+            new_steps = new_steps + 1
+            is_last_step = new_steps >= self.config.halt_max_steps
+
+            # Combine halt signals: Q or confidence or last-step
+            halted = is_last_step | (q_halt_logits > q_continue_logits) | (conf.squeeze(-1) >= self.config.glps_tau_halt)
+
+            # Exploration during training only
+            if self.training and (self.config.halt_max_steps > 1):
+                min_halt_steps = (torch.rand_like(q_halt_logits) < self.config.halt_exploration_prob) * torch.randint_like(new_steps, low=2, high=self.config.halt_max_steps + 1)
+                halted = halted & (new_steps >= min_halt_steps)
+                
+                # Optional target for Q-learning (kept similar to HRM)
+                next_conf = self.inner(new_inner_carry, new_current_data)[-1]
+                outputs["target_q_continue"] = torch.sigmoid(torch.where(is_last_step, q_halt_logits, torch.maximum(q_halt_logits, q_continue_logits)))
+            else:
+                # During eval, always use max_steps to ensure consistent reasoning depth (same as TRM/HRM eval behavior)
+                halted = is_last_step
+
+        return GLPS_ACTV1Carry(new_inner_carry, new_steps, halted, new_current_data), outputs
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v2/losses.py b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v2/losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..cdfb77718bc16f98cdd7da06eb5795c36b866aa7
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v2/losses.py
@@ -0,0 +1,102 @@
+from typing import Any, Tuple, Dict, Sequence, Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+import math
+
+IGNORE_LABEL_ID = -100
+
+
+def s(x, epsilon=1e-30):
+    return torch.where(
+        x<0,
+        1/(1-x+ epsilon),
+        x + 1
+    )
+
+
+def log_stablemax(x, dim=-1):
+    s_x = s(x)
+    return torch.log(s_x/torch.sum(s_x, dim=dim, keepdim=True))
+
+
+def stablemax_cross_entropy(logits, labels, ignore_index: int = -100, valid_mask=None):
+    logprobs = log_stablemax(logits.to(torch.float64), dim=-1)
+
+    if valid_mask is None:
+        valid_mask = (labels != ignore_index)
+    transformed_labels = torch.where(valid_mask, labels, 0)
+    prediction_logprobs = torch.gather(logprobs, index=transformed_labels.to(torch.long).unsqueeze(-1), dim=-1).squeeze(-1)
+
+    return -torch.where(valid_mask, prediction_logprobs, 0)
+
+
+def softmax_cross_entropy(logits, labels, ignore_index: int = -100):
+    # Cast logits to f32
+    # Flatten logits
+    return F.cross_entropy(logits.to(torch.float32).view(-1, logits.shape[-1]), labels.to(torch.long).view(-1), ignore_index=ignore_index, reduction="none").view(labels.shape)
+
+
+class ACTLossHead(nn.Module):
+    def __init__(self, model: nn.Module, loss_type: str):
+        super().__init__()
+        self.model = model
+        self.loss_fn = globals()[loss_type]
+        
+    def initial_carry(self, *args, **kwargs):
+        return self.model.initial_carry(*args, **kwargs)  # type: ignore
+
+    def forward(
+        self,
+        return_keys: Sequence[str],
+        # Model args
+        **model_kwargs,
+    ) -> Tuple[Any, torch.Tensor, Dict[str, torch.Tensor], Optional[Dict[str, torch.Tensor]], torch.Tensor]:
+        # Model logits
+        # B x SeqLen x D
+        new_carry, outputs = self.model(**model_kwargs)
+        labels = new_carry.current_data["labels"]
+
+        with torch.no_grad():
+            # Preds
+            outputs["preds"] = torch.argmax(outputs["logits"], dim=-1)
+
+            # Correctness
+            mask = (labels != IGNORE_LABEL_ID)
+            loss_counts = mask.sum(-1)
+            loss_divisor = loss_counts.clamp_min(1).unsqueeze(-1)  # Avoid NaNs in division
+
+            is_correct = mask & (torch.argmax(outputs["logits"], dim=-1) == labels)
+            seq_is_correct = is_correct.sum(-1) == loss_counts
+            
+            # Metrics (halted)
+            valid_metrics = new_carry.halted & (loss_counts > 0)
+            metrics = {
+                "count": valid_metrics.sum(),
+                
+                "accuracy":       torch.where(valid_metrics, (is_correct.to(torch.float32) / loss_divisor).sum(-1), 0).sum(),
+                "exact_accuracy": (valid_metrics & seq_is_correct).sum(),
+
+                "q_halt_accuracy": (valid_metrics & ((outputs["q_halt_logits"] >= 0) == seq_is_correct)).sum(),
+                "steps":          torch.where(valid_metrics, new_carry.steps, 0).sum(),
+            }
+
+        # Losses
+
+        lm_loss = (self.loss_fn(outputs["logits"], labels, ignore_index=IGNORE_LABEL_ID, valid_mask=mask) / loss_divisor).sum()
+        q_halt_loss = F.binary_cross_entropy_with_logits(outputs["q_halt_logits"], seq_is_correct.to(outputs["q_halt_logits"].dtype), reduction="sum")
+        metrics.update({
+            "lm_loss": lm_loss.detach(),
+            "q_halt_loss": q_halt_loss.detach(),
+        })
+        # Q continue (bootstrapping target loss); Alexia: This fits Q-learning, but seems totally unecessary
+        q_continue_loss = 0
+        if "target_q_continue" in outputs:
+            q_continue_loss = F.binary_cross_entropy_with_logits(outputs["q_continue_logits"], outputs["target_q_continue"], reduction="sum")
+
+            metrics["q_continue_loss"] = q_continue_loss.detach()
+        # Filter outputs for return
+        detached_outputs = {k: outputs[k].detach() for k in return_keys if k in outputs}
+
+        return new_carry, lm_loss + 0.5 * (q_halt_loss + q_continue_loss), metrics, detached_outputs, new_carry.halted.all()
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4/all_config.yaml b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4/all_config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d76907787512c38042cb9ac6b69ded0845914385
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4/all_config.yaml
@@ -0,0 +1,56 @@
+arch:
+  H_cycles: 3
+  H_layers: 2
+  L_cycles: 1
+  L_layers: 7
+  dep_rank: 64
+  dep_topk: 12
+  expansion: 4.0
+  forward_dtype: bfloat16
+  glps_dep_graph: true
+  glps_enabled: true
+  glps_fill_obvious: true
+  glps_global_propagate_on_low_conf: true
+  glps_max_targeted_iters: 4
+  glps_tau_halt: 0.95
+  glps_tau_uncertain: 0.8
+  glps_token_masking: true
+  halt_exploration_prob: 0.1
+  halt_max_steps: 16
+  hidden_size: 512
+  loss:
+    loss_type: stablemax_cross_entropy
+    name: losses@ACTLossHead
+  mlp_t: false
+  name: recursive_reasoning.glps@GLPS_ACTV1
+  num_heads: 8
+  pos_encodings: rope
+  puzzle_emb_ndim: 0
+  rms_norm_eps: 1.0e-05
+  rope_theta: 10000.0
+beta1: 0.9
+beta2: 0.95
+checkpoint_every_eval: true
+checkpoint_path: checkpoints/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4
+data_paths:
+- data/sudoku-extreme-1k-aug-1000
+data_paths_test: []
+ema: true
+ema_rate: 0.999
+epochs: 50000
+eval_interval: 5000
+eval_save_outputs: []
+evaluators: []
+freeze_weights: false
+global_batch_size: 768
+load_checkpoint: null
+lr: 0.0001
+lr_min_ratio: 1.0
+lr_warmup_steps: 2000
+min_eval_interval: 0
+project_name: Sudoku-extreme-1k-aug-1000-ACT-torch
+puzzle_emb_lr: 0.0001
+puzzle_emb_weight_decay: 1.0
+run_name: pretrain_glps_sudoku_v4
+seed: 0
+weight_decay: 1.0
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4/glps.py b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4/glps.py
new file mode 100644
index 0000000000000000000000000000000000000000..96e6de932e79e174cf773779293a643842e8a268
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4/glps.py
@@ -0,0 +1,409 @@
+
+from typing import Tuple, List, Dict
+from dataclasses import dataclass
+import math
+import torch
+import torch.nn.functional as F
+from torch import nn
+from pydantic import BaseModel
+
+# Reuse the same building blocks as HRM/TRM
+from models.common import trunc_normal_init_
+from models.layers import rms_norm, SwiGLU, Attention, RotaryEmbedding, CosSin, CastedEmbedding, CastedLinear
+from models.sparse_embedding import CastedSparseEmbedding
+
+"""
+Global-Local Predictive Solver (GLPS)
+------------------------------------
+A light-weight control-policy on top of the HRM/TRM style blocks:
+- H1: global scan -> certainty map
+- L1: fill-obvious (lock stable cells)
+- H2: dependency scoring over remaining cells
+- L2: targeted refinement (masked updates)
+- H3: energy-based confidence -> (optional) one global propagate sweep -> halt
+
+This file keeps parameter growth tiny: a few heads + (optional) low-rank dependency scorer.
+"""
+
+@dataclass
+class GLPS_ACTV1InnerCarry:
+    z_H: torch.Tensor
+    z_L: torch.Tensor
+
+@dataclass
+class GLPS_ACTV1Carry:
+    inner_carry: GLPS_ACTV1InnerCarry
+    steps: torch.Tensor
+    halted: torch.Tensor
+    current_data: Dict[str, torch.Tensor]
+
+class GLPS_ACTV1Config(BaseModel):
+    # Core IO / shapes
+    batch_size: int
+    seq_len: int
+    puzzle_emb_ndim: int = 0
+    num_puzzle_identifiers: int = 1
+    vocab_size: int = 256
+
+    # Cycle schedule
+    H_cycles: int = 3              # (scan -> refine -> check) typical
+    L_cycles: int = 1
+
+    # Depth
+    H_layers: int = 2
+    L_layers: int = 4
+
+    # Transformer config
+    hidden_size: int = 512
+    expansion: float = 2.0
+    num_heads: int = 8
+    pos_encodings: str = "rope"
+
+    rms_norm_eps: float = 1e-5
+    rope_theta: float = 10000.0
+
+    # ACT wrapper
+    halt_max_steps: int = 4
+    halt_exploration_prob: float = 0.1
+
+    forward_dtype: str = "bfloat16"
+
+    # Optional: use MLP on L instead of attention (matches HRM/TRM option)
+    mlp_t: bool = False
+
+    # ---- GLPS extras (tiny) ----
+    glps_enabled: bool = True
+    glps_fill_obvious: bool = True
+    glps_dep_graph: bool = True
+    glps_token_masking: bool = True
+    glps_global_propagate_on_low_conf: bool = True
+
+    glps_tau_halt: float = 0.95       # final confidence to halt
+    glps_tau_uncertain: float = 0.60  # cell-wise certainty threshold
+    glps_max_targeted_iters: int = 2  # small number: 1-2
+
+    # Dependency scorer (low rank bilinear)
+    dep_rank: int = 32
+    dep_topk: int = 8
+
+class GLPSBlock(nn.Module):
+    def __init__(self, config: GLPS_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        if self.config.mlp_t:
+            self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size)
+            self.mlp_t = SwiGLU(
+                hidden_size=self.config.seq_len + self.puzzle_emb_len, # treat sequence as channel
+                expansion=config.expansion,
+            )
+        else:
+            self.self_attn = Attention(
+                hidden_size=config.hidden_size,
+                head_dim=config.hidden_size // config.num_heads,
+                num_heads=config.num_heads,
+                num_key_value_heads=config.num_heads,
+                causal=False,
+            )
+        self.mlp = SwiGLU(hidden_size=config.hidden_size, expansion=config.expansion)
+        self.norm_eps = config.rms_norm_eps
+
+    def forward(self, cos_sin: CosSin, hidden_states: torch.Tensor) -> torch.Tensor:
+        if self.config.mlp_t:
+            # MLP over sequence dimension (mlp-t)
+            hidden_states = hidden_states.transpose(1, 2)
+            out = self.mlp_t(hidden_states)
+            hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+            hidden_states = hidden_states.transpose(1, 2)
+        else:
+            hidden_states = rms_norm(
+                hidden_states + self.self_attn(cos_sin=cos_sin, hidden_states=hidden_states),
+                variance_epsilon=self.norm_eps,
+            )
+        out = self.mlp(hidden_states)
+        hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+        return hidden_states
+
+class GLPSReasoningModule(nn.Module):
+    """Reasoning stack with optional masked updates (only update uncertain tokens)."""
+    def __init__(self, layers: List[GLPSBlock]):
+        super().__init__()
+        self.layers = torch.nn.ModuleList(layers)
+
+    def forward(self, hidden_states: torch.Tensor, input_injection: torch.Tensor, update_mask: torch.Tensor = None, **kwargs) -> torch.Tensor:
+        x = hidden_states
+        for layer in self.layers:
+            # Compute candidate update using injected context
+            y = layer(hidden_states=x + input_injection, **kwargs)
+            if update_mask is not None:
+                # Convex blend keeps frozen tokens unchanged
+                m = update_mask.to(x.dtype)[..., None]
+                x = x + m * (y - x)
+            else:
+                x = y
+        return x
+
+class GLPS_ACTV1_Inner(nn.Module):
+    def __init__(self, config: GLPS_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        self.forward_dtype = getattr(torch, self.config.forward_dtype)
+
+        # I/O
+        self.embed_scale  = math.sqrt(self.config.hidden_size)
+        embed_init_std = 1.0 / self.embed_scale
+
+        self.embed_tokens = CastedEmbedding(self.config.vocab_size, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+        self.lm_head      = CastedLinear(self.config.hidden_size, self.config.vocab_size, bias=False)
+        self.q_head       = CastedLinear(self.config.hidden_size, 2, bias=True)
+
+        # Puzzle emb (optional) — same convention as HRM/TRM
+        self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size)  # ceil div
+        if self.config.puzzle_emb_ndim > 0:
+            self.puzzle_emb = CastedSparseEmbedding(self.config.num_puzzle_identifiers, self.config.puzzle_emb_ndim, batch_size=self.config.batch_size, init_std=0, cast_to=self.forward_dtype)
+
+        # Positional encodings
+        if self.config.pos_encodings == "rope":
+            self.rotary_emb = RotaryEmbedding(dim=self.config.hidden_size // self.config.num_heads, max_position_embeddings=self.config.seq_len + self.puzzle_emb_len, base=self.config.rope_theta)
+        elif self.config.pos_encodings == "learned":
+            self.embed_pos = CastedEmbedding(self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+
+        # Reasoning stacks
+        self.H_level = GLPSReasoningModule(layers=[GLPSBlock(self.config) for _ in range(self.config.H_layers)])
+        self.L_level = GLPSReasoningModule(layers=[GLPSBlock(self.config) for _ in range(self.config.L_layers)])
+
+        # Initial states (match HRM/TRM style)
+        H_init = trunc_normal_init_(torch.empty(1, 1, self.config.hidden_size, dtype=self.forward_dtype), std=1.0)
+        L_init = trunc_normal_init_(torch.empty(1, 1, self.config.hidden_size, dtype=self.forward_dtype), std=1.0)
+        self.register_buffer("H_init", H_init, persistent=True)
+        self.register_buffer("L_init", L_init, persistent=True)
+
+        # GLPS small heads
+        self.candidate_head = CastedLinear(self.config.hidden_size, 16, bias=True)  # task-specific; for Sudoku you can slice to 9
+        self.certainty_head = CastedLinear(self.config.hidden_size, 1, bias=True)
+        self.energy_head    = CastedLinear(self.config.hidden_size, 1, bias=True)
+
+        # Low-rank dependency scorer (shared)
+        r = max(1, self.config.dep_rank)
+        self.dep_q = CastedLinear(self.config.hidden_size, r, bias=False)
+        self.dep_k = CastedLinear(self.config.hidden_size, r, bias=False)
+
+        # Q head init like HRM/TRM (near-zero -> easier bootstrapping)
+        with torch.no_grad():
+            self.q_head.weight.zero_()
+            self.q_head.bias.fill_(-5)
+
+    def _input_embeddings(self, input: torch.Tensor, puzzle_identifiers: torch.Tensor):
+        # Token embedding
+        embedding = self.embed_tokens(input.to(torch.int32))
+
+        # Puzzle embeddings
+        if self.config.puzzle_emb_ndim > 0:
+            puzzle_embedding = self.puzzle_emb(puzzle_identifiers)
+            pad_count = self.puzzle_emb_len * self.config.hidden_size - puzzle_embedding.shape[-1]
+            if pad_count > 0:
+                puzzle_embedding = F.pad(puzzle_embedding, (0, pad_count))
+            embedding = torch.cat((puzzle_embedding.view(-1, self.puzzle_emb_len, self.config.hidden_size), embedding), dim=-2)
+
+        # Position embeddings
+        if self.config.pos_encodings == "learned":
+            embedding = 0.707106781 * (embedding + self.embed_pos.embedding_weight.to(self.forward_dtype))
+
+        return self.embed_scale * embedding
+
+    def empty_carry(self, batch_size: int):
+        return GLPS_ACTV1InnerCarry(
+            z_H=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+            z_L=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+        )
+
+    def reset_carry(self, reset_flag: torch.Tensor, carry: GLPS_ACTV1InnerCarry):
+        # Explicitly expand buffers and mask to target shapes to avoid shape confusion
+        B, L, D = carry.z_H.shape
+        # Reduce/reset flag to per-batch boolean vector of shape [B]
+        if reset_flag.ndim == 1 and reset_flag.shape[0] == B:
+            reset_b = reset_flag.to(torch.bool)
+        else:
+            # If shape is [B, ...] reduce across non-batch dims; otherwise fallback to first B entries
+            try:
+                reset_b = reset_flag.reshape(B, -1).any(dim=1).to(torch.bool)
+            except Exception:
+                reset_b = reset_flag.reshape(-1)[:B].to(torch.bool)
+        m = reset_b.view(B, 1, 1)
+        mH = m.expand(B, L, D)
+        mL = mH  # same shape for z_L
+        H_init_exp = self.H_init.expand(B, L, D)
+        L_init_exp = self.L_init.expand(B, L, D)
+        return GLPS_ACTV1InnerCarry(
+            z_H=torch.where(mH, H_init_exp, carry.z_H),
+            z_L=torch.where(mL, L_init_exp, carry.z_L),
+        )
+
+    def _global_scan(self, z_L, z_H, input_embeddings, seq_info):
+        # One light pass to gather global signals
+        z_scan = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+        cand_logits = self.candidate_head(z_scan)              # [B, L, C]
+        certainty   = torch.sigmoid(self.certainty_head(z_scan))  # [B, L, 1]
+        return z_scan, cand_logits, certainty
+
+    def _build_dep_mask(self, z_ctx, uncertain_mask: torch.Tensor):
+        """Compute a dependency-based focus mask from a low-rank bilinear score.
+        uncertain_mask: [B, L] boolean mask of cells that are currently uncertain
+        Returns: dep_mask [B, L] boolean mask of cells to (re)update.
+        """
+        B, L, D = z_ctx.shape
+        # Project to low-rank space; use float32 to avoid dtype mismatches under torch.compile
+        Q = self.dep_q(z_ctx).to(torch.float32)   # [B, L, r]
+        K = self.dep_k(z_ctx).to(torch.float32)   # [B, L, r]
+        r = max(1, int(Q.shape[-1]))
+        sim = torch.matmul(Q, K.transpose(1, 2))  # [B, L, L] (float32)
+        sim = sim / math.sqrt(r)
+
+        # Aggregate influence from uncertain queries onto target tokens
+        src = uncertain_mask.to(sim.dtype).unsqueeze(1)      # [B, 1, L]
+        influence = torch.matmul(src, sim).squeeze(1)        # [B, L]
+
+        # Top-k influenced tokens per batch
+        topk = min(self.config.dep_topk, L)
+        vals, idx = torch.topk(influence, k=topk, dim=-1)    # [B, topk]
+        dep_mask = torch.zeros_like(uncertain_mask)
+        dep_mask.scatter_(1, idx, True)
+
+        # Always include uncertain cells themselves
+        dep_mask = dep_mask | uncertain_mask
+        return dep_mask
+
+    def forward(self, carry: GLPS_ACTV1InnerCarry, batch: Dict[str, torch.Tensor]):
+        seq_info = dict(cos_sin=getattr(self, "rotary_emb", None)() if hasattr(self, "rotary_emb") else None)
+
+        # Encode inputs
+        input_embeddings = self._input_embeddings(batch["inputs"], batch["puzzle_identifiers"])
+
+        # States
+        z_H, z_L = carry.z_H, carry.z_L
+
+        if not self.config.glps_enabled:
+            # Fallback to an HRM-like single-cycle grad update for compatibility
+            with torch.no_grad():
+                for _H in range(self.config.H_cycles - 1):
+                    for _L in range(self.config.L_cycles):
+                        z_L = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+                    z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+            # final grad step
+            for _L in range(self.config.L_cycles):
+                z_L = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+            z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+
+            # Outputs
+            new_carry = GLPS_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach())
+            logits = self.lm_head(z_H)[:, self.puzzle_emb_len:]
+            q_logits = self.q_head(z_H[:, 0]).to(torch.float32)
+            conf = torch.zeros_like(q_logits[..., :1]) + 0.5  # neutral
+            return new_carry, logits, (q_logits[..., 0], q_logits[..., 1]), conf
+
+        # ===== GLPS path =====
+        # H1: global scan (cheap)
+        with torch.no_grad():
+            z_scan, cand_logits, certainty = self._global_scan(z_L, z_H, input_embeddings, seq_info)
+
+        # L1: fill-obvious -> compute stable vs uncertain masks
+        if self.config.glps_fill_obvious:
+            obvious_mask = (certainty >= self.config.glps_tau_uncertain)  # [B, L, 1]
+        else:
+            obvious_mask = torch.zeros_like(certainty).bool()
+        stable_mask = obvious_mask.squeeze(-1)         # [B, L]
+        uncertain_mask = ~stable_mask                  # [B, L]
+
+        # H2: dependency prediction over remaining cells
+        if self.config.glps_dep_graph:
+            dep_mask = self._build_dep_mask(z_scan, uncertain_mask)   # [B, L]
+        else:
+            dep_mask = uncertain_mask
+
+        # L2: targeted refinement (a couple of masked iters)
+        update_mask = dep_mask if self.config.glps_token_masking else None
+        z = z_scan.detach()  # use scanned context as start
+        for _ in range(self.config.glps_max_targeted_iters):
+            z = self.L_level(z, z_H + input_embeddings, update_mask=update_mask, **seq_info)
+            # Refresh certainty to shrink mask (optional but cheap)
+            cert_now = torch.sigmoid(self.certainty_head(z)).squeeze(-1)
+            if self.config.glps_token_masking:
+                update_mask = dep_mask & (cert_now < self.config.glps_tau_uncertain)
+
+        # Merge into H and do a light H update with grad
+        z_L = z
+        z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+
+        # H3: energy/consistency -> confidence & optional global propagate
+        energy = torch.sigmoid(self.energy_head(z_H)).mean(dim=1)  # [B, 1]
+        conf = 1.0 - energy
+        need_sweep = (conf.squeeze(-1) < self.config.glps_tau_halt)
+        if self.config.glps_global_propagate_on_low_conf and need_sweep.any():
+            # one final full sweep only for rows needing it
+            maskB = need_sweep.view(-1, 1, 1)
+            zL2 = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+            zH2 = self.H_level(z_H, zL2, update_mask=None, **seq_info)
+            z_L = torch.where(maskB, zL2, z_L)
+            z_H = torch.where(maskB, zH2, z_H)
+
+        # Outputs
+        new_carry = GLPS_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach())
+        logits = self.lm_head(z_H)[:, self.puzzle_emb_len:]
+        q_logits = self.q_head(z_H[:, 0]).to(torch.float32)
+        return new_carry, logits, (q_logits[..., 0], q_logits[..., 1]), conf
+
+class GLPS_ACTV1(nn.Module):
+    """ACT-style wrapper that mixes Q-halt with GLPS confidence."""
+    def __init__(self, config_dict: dict):
+        super().__init__()
+        self.config = GLPS_ACTV1Config(**config_dict)
+        self.inner = GLPS_ACTV1_Inner(self.config)
+
+    @property
+    def puzzle_emb(self):
+        return self.inner.puzzle_emb
+
+    def initial_carry(self, batch: Dict[str, torch.Tensor]):
+        batch_size = batch["inputs"].shape[0]
+        return GLPS_ACTV1Carry(
+            inner_carry=self.inner.empty_carry(batch_size),
+            steps=torch.zeros((batch_size,), dtype=torch.int32),
+            halted=torch.ones((batch_size,), dtype=torch.bool),  # start halted to force reset on first pass
+            current_data={k: torch.empty_like(v) for k, v in batch.items()}
+        )
+
+    def forward(self, carry: GLPS_ACTV1Carry, batch: Dict[str, torch.Tensor]):
+        # Reset halted seqs
+        new_inner_carry = self.inner.reset_carry(carry.halted, carry.inner_carry)
+        new_steps = torch.where(carry.halted, 0, carry.steps)
+        new_current_data = {k: torch.where(carry.halted.view((-1,) + (1,) * (batch[k].ndim - 1)), batch[k], v) for k, v in carry.current_data.items()}
+
+        # Inner step
+        new_inner_carry, logits, (q_halt_logits, q_continue_logits), conf = self.inner(new_inner_carry, new_current_data)
+
+        outputs = {
+            "logits": logits,
+            "q_halt_logits": q_halt_logits,
+            "q_continue_logits": q_continue_logits,
+            "conf": conf.squeeze(-1),
+        }
+
+        with torch.no_grad():
+            new_steps = new_steps + 1
+            is_last_step = new_steps >= self.config.halt_max_steps
+
+            # Combine halt signals: Q or confidence or last-step
+            halted = is_last_step | (q_halt_logits > q_continue_logits) | (conf.squeeze(-1) >= self.config.glps_tau_halt)
+
+            # Exploration during training only
+            if self.training and (self.config.halt_max_steps > 1):
+                min_halt_steps = (torch.rand_like(q_halt_logits) < self.config.halt_exploration_prob) * torch.randint_like(new_steps, low=2, high=self.config.halt_max_steps + 1)
+                halted = halted & (new_steps >= min_halt_steps)
+                
+                # Optional target for Q-learning (kept similar to HRM)
+                next_conf = self.inner(new_inner_carry, new_current_data)[-1]
+                outputs["target_q_continue"] = torch.sigmoid(torch.where(is_last_step, q_halt_logits, torch.maximum(q_halt_logits, q_continue_logits)))
+            else:
+                # During eval, always use max_steps to ensure consistent reasoning depth (same as TRM/HRM eval behavior)
+                halted = is_last_step
+
+        return GLPS_ACTV1Carry(new_inner_carry, new_steps, halted, new_current_data), outputs
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4/losses.py b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4/losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..cdfb77718bc16f98cdd7da06eb5795c36b866aa7
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4/losses.py
@@ -0,0 +1,102 @@
+from typing import Any, Tuple, Dict, Sequence, Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+import math
+
+IGNORE_LABEL_ID = -100
+
+
+def s(x, epsilon=1e-30):
+    return torch.where(
+        x<0,
+        1/(1-x+ epsilon),
+        x + 1
+    )
+
+
+def log_stablemax(x, dim=-1):
+    s_x = s(x)
+    return torch.log(s_x/torch.sum(s_x, dim=dim, keepdim=True))
+
+
+def stablemax_cross_entropy(logits, labels, ignore_index: int = -100, valid_mask=None):
+    logprobs = log_stablemax(logits.to(torch.float64), dim=-1)
+
+    if valid_mask is None:
+        valid_mask = (labels != ignore_index)
+    transformed_labels = torch.where(valid_mask, labels, 0)
+    prediction_logprobs = torch.gather(logprobs, index=transformed_labels.to(torch.long).unsqueeze(-1), dim=-1).squeeze(-1)
+
+    return -torch.where(valid_mask, prediction_logprobs, 0)
+
+
+def softmax_cross_entropy(logits, labels, ignore_index: int = -100):
+    # Cast logits to f32
+    # Flatten logits
+    return F.cross_entropy(logits.to(torch.float32).view(-1, logits.shape[-1]), labels.to(torch.long).view(-1), ignore_index=ignore_index, reduction="none").view(labels.shape)
+
+
+class ACTLossHead(nn.Module):
+    def __init__(self, model: nn.Module, loss_type: str):
+        super().__init__()
+        self.model = model
+        self.loss_fn = globals()[loss_type]
+        
+    def initial_carry(self, *args, **kwargs):
+        return self.model.initial_carry(*args, **kwargs)  # type: ignore
+
+    def forward(
+        self,
+        return_keys: Sequence[str],
+        # Model args
+        **model_kwargs,
+    ) -> Tuple[Any, torch.Tensor, Dict[str, torch.Tensor], Optional[Dict[str, torch.Tensor]], torch.Tensor]:
+        # Model logits
+        # B x SeqLen x D
+        new_carry, outputs = self.model(**model_kwargs)
+        labels = new_carry.current_data["labels"]
+
+        with torch.no_grad():
+            # Preds
+            outputs["preds"] = torch.argmax(outputs["logits"], dim=-1)
+
+            # Correctness
+            mask = (labels != IGNORE_LABEL_ID)
+            loss_counts = mask.sum(-1)
+            loss_divisor = loss_counts.clamp_min(1).unsqueeze(-1)  # Avoid NaNs in division
+
+            is_correct = mask & (torch.argmax(outputs["logits"], dim=-1) == labels)
+            seq_is_correct = is_correct.sum(-1) == loss_counts
+            
+            # Metrics (halted)
+            valid_metrics = new_carry.halted & (loss_counts > 0)
+            metrics = {
+                "count": valid_metrics.sum(),
+                
+                "accuracy":       torch.where(valid_metrics, (is_correct.to(torch.float32) / loss_divisor).sum(-1), 0).sum(),
+                "exact_accuracy": (valid_metrics & seq_is_correct).sum(),
+
+                "q_halt_accuracy": (valid_metrics & ((outputs["q_halt_logits"] >= 0) == seq_is_correct)).sum(),
+                "steps":          torch.where(valid_metrics, new_carry.steps, 0).sum(),
+            }
+
+        # Losses
+
+        lm_loss = (self.loss_fn(outputs["logits"], labels, ignore_index=IGNORE_LABEL_ID, valid_mask=mask) / loss_divisor).sum()
+        q_halt_loss = F.binary_cross_entropy_with_logits(outputs["q_halt_logits"], seq_is_correct.to(outputs["q_halt_logits"].dtype), reduction="sum")
+        metrics.update({
+            "lm_loss": lm_loss.detach(),
+            "q_halt_loss": q_halt_loss.detach(),
+        })
+        # Q continue (bootstrapping target loss); Alexia: This fits Q-learning, but seems totally unecessary
+        q_continue_loss = 0
+        if "target_q_continue" in outputs:
+            q_continue_loss = F.binary_cross_entropy_with_logits(outputs["q_continue_logits"], outputs["target_q_continue"], reduction="sum")
+
+            metrics["q_continue_loss"] = q_continue_loss.detach()
+        # Filter outputs for return
+        detached_outputs = {k: outputs[k].detach() for k in return_keys if k in outputs}
+
+        return new_carry, lm_loss + 0.5 * (q_halt_loss + q_continue_loss), metrics, detached_outputs, new_carry.halted.all()
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_decay/all_config.yaml b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_decay/all_config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..13ccf34a9070b46351aea3262fe041ec694b679e
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_decay/all_config.yaml
@@ -0,0 +1,56 @@
+arch:
+  H_cycles: 3
+  H_layers: 2
+  L_cycles: 1
+  L_layers: 7
+  dep_rank: 64
+  dep_topk: 12
+  expansion: 4.0
+  forward_dtype: bfloat16
+  glps_dep_graph: true
+  glps_enabled: true
+  glps_fill_obvious: true
+  glps_global_propagate_on_low_conf: true
+  glps_max_targeted_iters: 4
+  glps_tau_halt: 0.95
+  glps_tau_uncertain: 0.8
+  glps_token_masking: true
+  halt_exploration_prob: 0.1
+  halt_max_steps: 16
+  hidden_size: 512
+  loss:
+    loss_type: stablemax_cross_entropy
+    name: losses@ACTLossHead
+  mlp_t: false
+  name: recursive_reasoning.glps@GLPS_ACTV1
+  num_heads: 8
+  pos_encodings: rope
+  puzzle_emb_ndim: 0
+  rms_norm_eps: 1.0e-05
+  rope_theta: 10000.0
+beta1: 0.9
+beta2: 0.95
+checkpoint_every_eval: true
+checkpoint_path: checkpoints/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_decay
+data_paths:
+- data/sudoku-extreme-1k-aug-1000
+data_paths_test: []
+ema: true
+ema_rate: 0.999
+epochs: 50000
+eval_interval: 5000
+eval_save_outputs: []
+evaluators: []
+freeze_weights: false
+global_batch_size: 768
+load_checkpoint: checkpoints/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_decay/step_45570
+lr: 0.0001
+lr_min_ratio: 0.1
+lr_warmup_steps: 2000
+min_eval_interval: 0
+project_name: Sudoku-extreme-1k-aug-1000-ACT-torch
+puzzle_emb_lr: 0.0001
+puzzle_emb_weight_decay: 1.0
+run_name: pretrain_glps_sudoku_v4_decay
+seed: 0
+weight_decay: 1.0
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_decay/glps.py b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_decay/glps.py
new file mode 100644
index 0000000000000000000000000000000000000000..96e6de932e79e174cf773779293a643842e8a268
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_decay/glps.py
@@ -0,0 +1,409 @@
+
+from typing import Tuple, List, Dict
+from dataclasses import dataclass
+import math
+import torch
+import torch.nn.functional as F
+from torch import nn
+from pydantic import BaseModel
+
+# Reuse the same building blocks as HRM/TRM
+from models.common import trunc_normal_init_
+from models.layers import rms_norm, SwiGLU, Attention, RotaryEmbedding, CosSin, CastedEmbedding, CastedLinear
+from models.sparse_embedding import CastedSparseEmbedding
+
+"""
+Global-Local Predictive Solver (GLPS)
+------------------------------------
+A light-weight control-policy on top of the HRM/TRM style blocks:
+- H1: global scan -> certainty map
+- L1: fill-obvious (lock stable cells)
+- H2: dependency scoring over remaining cells
+- L2: targeted refinement (masked updates)
+- H3: energy-based confidence -> (optional) one global propagate sweep -> halt
+
+This file keeps parameter growth tiny: a few heads + (optional) low-rank dependency scorer.
+"""
+
+@dataclass
+class GLPS_ACTV1InnerCarry:
+    z_H: torch.Tensor
+    z_L: torch.Tensor
+
+@dataclass
+class GLPS_ACTV1Carry:
+    inner_carry: GLPS_ACTV1InnerCarry
+    steps: torch.Tensor
+    halted: torch.Tensor
+    current_data: Dict[str, torch.Tensor]
+
+class GLPS_ACTV1Config(BaseModel):
+    # Core IO / shapes
+    batch_size: int
+    seq_len: int
+    puzzle_emb_ndim: int = 0
+    num_puzzle_identifiers: int = 1
+    vocab_size: int = 256
+
+    # Cycle schedule
+    H_cycles: int = 3              # (scan -> refine -> check) typical
+    L_cycles: int = 1
+
+    # Depth
+    H_layers: int = 2
+    L_layers: int = 4
+
+    # Transformer config
+    hidden_size: int = 512
+    expansion: float = 2.0
+    num_heads: int = 8
+    pos_encodings: str = "rope"
+
+    rms_norm_eps: float = 1e-5
+    rope_theta: float = 10000.0
+
+    # ACT wrapper
+    halt_max_steps: int = 4
+    halt_exploration_prob: float = 0.1
+
+    forward_dtype: str = "bfloat16"
+
+    # Optional: use MLP on L instead of attention (matches HRM/TRM option)
+    mlp_t: bool = False
+
+    # ---- GLPS extras (tiny) ----
+    glps_enabled: bool = True
+    glps_fill_obvious: bool = True
+    glps_dep_graph: bool = True
+    glps_token_masking: bool = True
+    glps_global_propagate_on_low_conf: bool = True
+
+    glps_tau_halt: float = 0.95       # final confidence to halt
+    glps_tau_uncertain: float = 0.60  # cell-wise certainty threshold
+    glps_max_targeted_iters: int = 2  # small number: 1-2
+
+    # Dependency scorer (low rank bilinear)
+    dep_rank: int = 32
+    dep_topk: int = 8
+
+class GLPSBlock(nn.Module):
+    def __init__(self, config: GLPS_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        if self.config.mlp_t:
+            self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size)
+            self.mlp_t = SwiGLU(
+                hidden_size=self.config.seq_len + self.puzzle_emb_len, # treat sequence as channel
+                expansion=config.expansion,
+            )
+        else:
+            self.self_attn = Attention(
+                hidden_size=config.hidden_size,
+                head_dim=config.hidden_size // config.num_heads,
+                num_heads=config.num_heads,
+                num_key_value_heads=config.num_heads,
+                causal=False,
+            )
+        self.mlp = SwiGLU(hidden_size=config.hidden_size, expansion=config.expansion)
+        self.norm_eps = config.rms_norm_eps
+
+    def forward(self, cos_sin: CosSin, hidden_states: torch.Tensor) -> torch.Tensor:
+        if self.config.mlp_t:
+            # MLP over sequence dimension (mlp-t)
+            hidden_states = hidden_states.transpose(1, 2)
+            out = self.mlp_t(hidden_states)
+            hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+            hidden_states = hidden_states.transpose(1, 2)
+        else:
+            hidden_states = rms_norm(
+                hidden_states + self.self_attn(cos_sin=cos_sin, hidden_states=hidden_states),
+                variance_epsilon=self.norm_eps,
+            )
+        out = self.mlp(hidden_states)
+        hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+        return hidden_states
+
+class GLPSReasoningModule(nn.Module):
+    """Reasoning stack with optional masked updates (only update uncertain tokens)."""
+    def __init__(self, layers: List[GLPSBlock]):
+        super().__init__()
+        self.layers = torch.nn.ModuleList(layers)
+
+    def forward(self, hidden_states: torch.Tensor, input_injection: torch.Tensor, update_mask: torch.Tensor = None, **kwargs) -> torch.Tensor:
+        x = hidden_states
+        for layer in self.layers:
+            # Compute candidate update using injected context
+            y = layer(hidden_states=x + input_injection, **kwargs)
+            if update_mask is not None:
+                # Convex blend keeps frozen tokens unchanged
+                m = update_mask.to(x.dtype)[..., None]
+                x = x + m * (y - x)
+            else:
+                x = y
+        return x
+
+class GLPS_ACTV1_Inner(nn.Module):
+    def __init__(self, config: GLPS_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        self.forward_dtype = getattr(torch, self.config.forward_dtype)
+
+        # I/O
+        self.embed_scale  = math.sqrt(self.config.hidden_size)
+        embed_init_std = 1.0 / self.embed_scale
+
+        self.embed_tokens = CastedEmbedding(self.config.vocab_size, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+        self.lm_head      = CastedLinear(self.config.hidden_size, self.config.vocab_size, bias=False)
+        self.q_head       = CastedLinear(self.config.hidden_size, 2, bias=True)
+
+        # Puzzle emb (optional) — same convention as HRM/TRM
+        self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size)  # ceil div
+        if self.config.puzzle_emb_ndim > 0:
+            self.puzzle_emb = CastedSparseEmbedding(self.config.num_puzzle_identifiers, self.config.puzzle_emb_ndim, batch_size=self.config.batch_size, init_std=0, cast_to=self.forward_dtype)
+
+        # Positional encodings
+        if self.config.pos_encodings == "rope":
+            self.rotary_emb = RotaryEmbedding(dim=self.config.hidden_size // self.config.num_heads, max_position_embeddings=self.config.seq_len + self.puzzle_emb_len, base=self.config.rope_theta)
+        elif self.config.pos_encodings == "learned":
+            self.embed_pos = CastedEmbedding(self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+
+        # Reasoning stacks
+        self.H_level = GLPSReasoningModule(layers=[GLPSBlock(self.config) for _ in range(self.config.H_layers)])
+        self.L_level = GLPSReasoningModule(layers=[GLPSBlock(self.config) for _ in range(self.config.L_layers)])
+
+        # Initial states (match HRM/TRM style)
+        H_init = trunc_normal_init_(torch.empty(1, 1, self.config.hidden_size, dtype=self.forward_dtype), std=1.0)
+        L_init = trunc_normal_init_(torch.empty(1, 1, self.config.hidden_size, dtype=self.forward_dtype), std=1.0)
+        self.register_buffer("H_init", H_init, persistent=True)
+        self.register_buffer("L_init", L_init, persistent=True)
+
+        # GLPS small heads
+        self.candidate_head = CastedLinear(self.config.hidden_size, 16, bias=True)  # task-specific; for Sudoku you can slice to 9
+        self.certainty_head = CastedLinear(self.config.hidden_size, 1, bias=True)
+        self.energy_head    = CastedLinear(self.config.hidden_size, 1, bias=True)
+
+        # Low-rank dependency scorer (shared)
+        r = max(1, self.config.dep_rank)
+        self.dep_q = CastedLinear(self.config.hidden_size, r, bias=False)
+        self.dep_k = CastedLinear(self.config.hidden_size, r, bias=False)
+
+        # Q head init like HRM/TRM (near-zero -> easier bootstrapping)
+        with torch.no_grad():
+            self.q_head.weight.zero_()
+            self.q_head.bias.fill_(-5)
+
+    def _input_embeddings(self, input: torch.Tensor, puzzle_identifiers: torch.Tensor):
+        # Token embedding
+        embedding = self.embed_tokens(input.to(torch.int32))
+
+        # Puzzle embeddings
+        if self.config.puzzle_emb_ndim > 0:
+            puzzle_embedding = self.puzzle_emb(puzzle_identifiers)
+            pad_count = self.puzzle_emb_len * self.config.hidden_size - puzzle_embedding.shape[-1]
+            if pad_count > 0:
+                puzzle_embedding = F.pad(puzzle_embedding, (0, pad_count))
+            embedding = torch.cat((puzzle_embedding.view(-1, self.puzzle_emb_len, self.config.hidden_size), embedding), dim=-2)
+
+        # Position embeddings
+        if self.config.pos_encodings == "learned":
+            embedding = 0.707106781 * (embedding + self.embed_pos.embedding_weight.to(self.forward_dtype))
+
+        return self.embed_scale * embedding
+
+    def empty_carry(self, batch_size: int):
+        return GLPS_ACTV1InnerCarry(
+            z_H=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+            z_L=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+        )
+
+    def reset_carry(self, reset_flag: torch.Tensor, carry: GLPS_ACTV1InnerCarry):
+        # Explicitly expand buffers and mask to target shapes to avoid shape confusion
+        B, L, D = carry.z_H.shape
+        # Reduce/reset flag to per-batch boolean vector of shape [B]
+        if reset_flag.ndim == 1 and reset_flag.shape[0] == B:
+            reset_b = reset_flag.to(torch.bool)
+        else:
+            # If shape is [B, ...] reduce across non-batch dims; otherwise fallback to first B entries
+            try:
+                reset_b = reset_flag.reshape(B, -1).any(dim=1).to(torch.bool)
+            except Exception:
+                reset_b = reset_flag.reshape(-1)[:B].to(torch.bool)
+        m = reset_b.view(B, 1, 1)
+        mH = m.expand(B, L, D)
+        mL = mH  # same shape for z_L
+        H_init_exp = self.H_init.expand(B, L, D)
+        L_init_exp = self.L_init.expand(B, L, D)
+        return GLPS_ACTV1InnerCarry(
+            z_H=torch.where(mH, H_init_exp, carry.z_H),
+            z_L=torch.where(mL, L_init_exp, carry.z_L),
+        )
+
+    def _global_scan(self, z_L, z_H, input_embeddings, seq_info):
+        # One light pass to gather global signals
+        z_scan = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+        cand_logits = self.candidate_head(z_scan)              # [B, L, C]
+        certainty   = torch.sigmoid(self.certainty_head(z_scan))  # [B, L, 1]
+        return z_scan, cand_logits, certainty
+
+    def _build_dep_mask(self, z_ctx, uncertain_mask: torch.Tensor):
+        """Compute a dependency-based focus mask from a low-rank bilinear score.
+        uncertain_mask: [B, L] boolean mask of cells that are currently uncertain
+        Returns: dep_mask [B, L] boolean mask of cells to (re)update.
+        """
+        B, L, D = z_ctx.shape
+        # Project to low-rank space; use float32 to avoid dtype mismatches under torch.compile
+        Q = self.dep_q(z_ctx).to(torch.float32)   # [B, L, r]
+        K = self.dep_k(z_ctx).to(torch.float32)   # [B, L, r]
+        r = max(1, int(Q.shape[-1]))
+        sim = torch.matmul(Q, K.transpose(1, 2))  # [B, L, L] (float32)
+        sim = sim / math.sqrt(r)
+
+        # Aggregate influence from uncertain queries onto target tokens
+        src = uncertain_mask.to(sim.dtype).unsqueeze(1)      # [B, 1, L]
+        influence = torch.matmul(src, sim).squeeze(1)        # [B, L]
+
+        # Top-k influenced tokens per batch
+        topk = min(self.config.dep_topk, L)
+        vals, idx = torch.topk(influence, k=topk, dim=-1)    # [B, topk]
+        dep_mask = torch.zeros_like(uncertain_mask)
+        dep_mask.scatter_(1, idx, True)
+
+        # Always include uncertain cells themselves
+        dep_mask = dep_mask | uncertain_mask
+        return dep_mask
+
+    def forward(self, carry: GLPS_ACTV1InnerCarry, batch: Dict[str, torch.Tensor]):
+        seq_info = dict(cos_sin=getattr(self, "rotary_emb", None)() if hasattr(self, "rotary_emb") else None)
+
+        # Encode inputs
+        input_embeddings = self._input_embeddings(batch["inputs"], batch["puzzle_identifiers"])
+
+        # States
+        z_H, z_L = carry.z_H, carry.z_L
+
+        if not self.config.glps_enabled:
+            # Fallback to an HRM-like single-cycle grad update for compatibility
+            with torch.no_grad():
+                for _H in range(self.config.H_cycles - 1):
+                    for _L in range(self.config.L_cycles):
+                        z_L = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+                    z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+            # final grad step
+            for _L in range(self.config.L_cycles):
+                z_L = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+            z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+
+            # Outputs
+            new_carry = GLPS_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach())
+            logits = self.lm_head(z_H)[:, self.puzzle_emb_len:]
+            q_logits = self.q_head(z_H[:, 0]).to(torch.float32)
+            conf = torch.zeros_like(q_logits[..., :1]) + 0.5  # neutral
+            return new_carry, logits, (q_logits[..., 0], q_logits[..., 1]), conf
+
+        # ===== GLPS path =====
+        # H1: global scan (cheap)
+        with torch.no_grad():
+            z_scan, cand_logits, certainty = self._global_scan(z_L, z_H, input_embeddings, seq_info)
+
+        # L1: fill-obvious -> compute stable vs uncertain masks
+        if self.config.glps_fill_obvious:
+            obvious_mask = (certainty >= self.config.glps_tau_uncertain)  # [B, L, 1]
+        else:
+            obvious_mask = torch.zeros_like(certainty).bool()
+        stable_mask = obvious_mask.squeeze(-1)         # [B, L]
+        uncertain_mask = ~stable_mask                  # [B, L]
+
+        # H2: dependency prediction over remaining cells
+        if self.config.glps_dep_graph:
+            dep_mask = self._build_dep_mask(z_scan, uncertain_mask)   # [B, L]
+        else:
+            dep_mask = uncertain_mask
+
+        # L2: targeted refinement (a couple of masked iters)
+        update_mask = dep_mask if self.config.glps_token_masking else None
+        z = z_scan.detach()  # use scanned context as start
+        for _ in range(self.config.glps_max_targeted_iters):
+            z = self.L_level(z, z_H + input_embeddings, update_mask=update_mask, **seq_info)
+            # Refresh certainty to shrink mask (optional but cheap)
+            cert_now = torch.sigmoid(self.certainty_head(z)).squeeze(-1)
+            if self.config.glps_token_masking:
+                update_mask = dep_mask & (cert_now < self.config.glps_tau_uncertain)
+
+        # Merge into H and do a light H update with grad
+        z_L = z
+        z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+
+        # H3: energy/consistency -> confidence & optional global propagate
+        energy = torch.sigmoid(self.energy_head(z_H)).mean(dim=1)  # [B, 1]
+        conf = 1.0 - energy
+        need_sweep = (conf.squeeze(-1) < self.config.glps_tau_halt)
+        if self.config.glps_global_propagate_on_low_conf and need_sweep.any():
+            # one final full sweep only for rows needing it
+            maskB = need_sweep.view(-1, 1, 1)
+            zL2 = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+            zH2 = self.H_level(z_H, zL2, update_mask=None, **seq_info)
+            z_L = torch.where(maskB, zL2, z_L)
+            z_H = torch.where(maskB, zH2, z_H)
+
+        # Outputs
+        new_carry = GLPS_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach())
+        logits = self.lm_head(z_H)[:, self.puzzle_emb_len:]
+        q_logits = self.q_head(z_H[:, 0]).to(torch.float32)
+        return new_carry, logits, (q_logits[..., 0], q_logits[..., 1]), conf
+
+class GLPS_ACTV1(nn.Module):
+    """ACT-style wrapper that mixes Q-halt with GLPS confidence."""
+    def __init__(self, config_dict: dict):
+        super().__init__()
+        self.config = GLPS_ACTV1Config(**config_dict)
+        self.inner = GLPS_ACTV1_Inner(self.config)
+
+    @property
+    def puzzle_emb(self):
+        return self.inner.puzzle_emb
+
+    def initial_carry(self, batch: Dict[str, torch.Tensor]):
+        batch_size = batch["inputs"].shape[0]
+        return GLPS_ACTV1Carry(
+            inner_carry=self.inner.empty_carry(batch_size),
+            steps=torch.zeros((batch_size,), dtype=torch.int32),
+            halted=torch.ones((batch_size,), dtype=torch.bool),  # start halted to force reset on first pass
+            current_data={k: torch.empty_like(v) for k, v in batch.items()}
+        )
+
+    def forward(self, carry: GLPS_ACTV1Carry, batch: Dict[str, torch.Tensor]):
+        # Reset halted seqs
+        new_inner_carry = self.inner.reset_carry(carry.halted, carry.inner_carry)
+        new_steps = torch.where(carry.halted, 0, carry.steps)
+        new_current_data = {k: torch.where(carry.halted.view((-1,) + (1,) * (batch[k].ndim - 1)), batch[k], v) for k, v in carry.current_data.items()}
+
+        # Inner step
+        new_inner_carry, logits, (q_halt_logits, q_continue_logits), conf = self.inner(new_inner_carry, new_current_data)
+
+        outputs = {
+            "logits": logits,
+            "q_halt_logits": q_halt_logits,
+            "q_continue_logits": q_continue_logits,
+            "conf": conf.squeeze(-1),
+        }
+
+        with torch.no_grad():
+            new_steps = new_steps + 1
+            is_last_step = new_steps >= self.config.halt_max_steps
+
+            # Combine halt signals: Q or confidence or last-step
+            halted = is_last_step | (q_halt_logits > q_continue_logits) | (conf.squeeze(-1) >= self.config.glps_tau_halt)
+
+            # Exploration during training only
+            if self.training and (self.config.halt_max_steps > 1):
+                min_halt_steps = (torch.rand_like(q_halt_logits) < self.config.halt_exploration_prob) * torch.randint_like(new_steps, low=2, high=self.config.halt_max_steps + 1)
+                halted = halted & (new_steps >= min_halt_steps)
+                
+                # Optional target for Q-learning (kept similar to HRM)
+                next_conf = self.inner(new_inner_carry, new_current_data)[-1]
+                outputs["target_q_continue"] = torch.sigmoid(torch.where(is_last_step, q_halt_logits, torch.maximum(q_halt_logits, q_continue_logits)))
+            else:
+                # During eval, always use max_steps to ensure consistent reasoning depth (same as TRM/HRM eval behavior)
+                halted = is_last_step
+
+        return GLPS_ACTV1Carry(new_inner_carry, new_steps, halted, new_current_data), outputs
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_decay/losses.py b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_decay/losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..cdfb77718bc16f98cdd7da06eb5795c36b866aa7
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_decay/losses.py
@@ -0,0 +1,102 @@
+from typing import Any, Tuple, Dict, Sequence, Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+import math
+
+IGNORE_LABEL_ID = -100
+
+
+def s(x, epsilon=1e-30):
+    return torch.where(
+        x<0,
+        1/(1-x+ epsilon),
+        x + 1
+    )
+
+
+def log_stablemax(x, dim=-1):
+    s_x = s(x)
+    return torch.log(s_x/torch.sum(s_x, dim=dim, keepdim=True))
+
+
+def stablemax_cross_entropy(logits, labels, ignore_index: int = -100, valid_mask=None):
+    logprobs = log_stablemax(logits.to(torch.float64), dim=-1)
+
+    if valid_mask is None:
+        valid_mask = (labels != ignore_index)
+    transformed_labels = torch.where(valid_mask, labels, 0)
+    prediction_logprobs = torch.gather(logprobs, index=transformed_labels.to(torch.long).unsqueeze(-1), dim=-1).squeeze(-1)
+
+    return -torch.where(valid_mask, prediction_logprobs, 0)
+
+
+def softmax_cross_entropy(logits, labels, ignore_index: int = -100):
+    # Cast logits to f32
+    # Flatten logits
+    return F.cross_entropy(logits.to(torch.float32).view(-1, logits.shape[-1]), labels.to(torch.long).view(-1), ignore_index=ignore_index, reduction="none").view(labels.shape)
+
+
+class ACTLossHead(nn.Module):
+    def __init__(self, model: nn.Module, loss_type: str):
+        super().__init__()
+        self.model = model
+        self.loss_fn = globals()[loss_type]
+        
+    def initial_carry(self, *args, **kwargs):
+        return self.model.initial_carry(*args, **kwargs)  # type: ignore
+
+    def forward(
+        self,
+        return_keys: Sequence[str],
+        # Model args
+        **model_kwargs,
+    ) -> Tuple[Any, torch.Tensor, Dict[str, torch.Tensor], Optional[Dict[str, torch.Tensor]], torch.Tensor]:
+        # Model logits
+        # B x SeqLen x D
+        new_carry, outputs = self.model(**model_kwargs)
+        labels = new_carry.current_data["labels"]
+
+        with torch.no_grad():
+            # Preds
+            outputs["preds"] = torch.argmax(outputs["logits"], dim=-1)
+
+            # Correctness
+            mask = (labels != IGNORE_LABEL_ID)
+            loss_counts = mask.sum(-1)
+            loss_divisor = loss_counts.clamp_min(1).unsqueeze(-1)  # Avoid NaNs in division
+
+            is_correct = mask & (torch.argmax(outputs["logits"], dim=-1) == labels)
+            seq_is_correct = is_correct.sum(-1) == loss_counts
+            
+            # Metrics (halted)
+            valid_metrics = new_carry.halted & (loss_counts > 0)
+            metrics = {
+                "count": valid_metrics.sum(),
+                
+                "accuracy":       torch.where(valid_metrics, (is_correct.to(torch.float32) / loss_divisor).sum(-1), 0).sum(),
+                "exact_accuracy": (valid_metrics & seq_is_correct).sum(),
+
+                "q_halt_accuracy": (valid_metrics & ((outputs["q_halt_logits"] >= 0) == seq_is_correct)).sum(),
+                "steps":          torch.where(valid_metrics, new_carry.steps, 0).sum(),
+            }
+
+        # Losses
+
+        lm_loss = (self.loss_fn(outputs["logits"], labels, ignore_index=IGNORE_LABEL_ID, valid_mask=mask) / loss_divisor).sum()
+        q_halt_loss = F.binary_cross_entropy_with_logits(outputs["q_halt_logits"], seq_is_correct.to(outputs["q_halt_logits"].dtype), reduction="sum")
+        metrics.update({
+            "lm_loss": lm_loss.detach(),
+            "q_halt_loss": q_halt_loss.detach(),
+        })
+        # Q continue (bootstrapping target loss); Alexia: This fits Q-learning, but seems totally unecessary
+        q_continue_loss = 0
+        if "target_q_continue" in outputs:
+            q_continue_loss = F.binary_cross_entropy_with_logits(outputs["q_continue_logits"], outputs["target_q_continue"], reduction="sum")
+
+            metrics["q_continue_loss"] = q_continue_loss.detach()
+        # Filter outputs for return
+        detached_outputs = {k: outputs[k].detach() for k in return_keys if k in outputs}
+
+        return new_carry, lm_loss + 0.5 * (q_halt_loss + q_continue_loss), metrics, detached_outputs, new_carry.halted.all()
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_decay_ft10k/all_config.yaml b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_decay_ft10k/all_config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..5dfc7bfedc3dc3253b1161452bd6d1b5eda6c4ef
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_decay_ft10k/all_config.yaml
@@ -0,0 +1,56 @@
+arch:
+  H_cycles: 3
+  H_layers: 2
+  L_cycles: 1
+  L_layers: 7
+  dep_rank: 64
+  dep_topk: 12
+  expansion: 4.0
+  forward_dtype: bfloat16
+  glps_dep_graph: true
+  glps_enabled: true
+  glps_fill_obvious: true
+  glps_global_propagate_on_low_conf: true
+  glps_max_targeted_iters: 4
+  glps_tau_halt: 0.9
+  glps_tau_uncertain: 0.8
+  glps_token_masking: true
+  halt_exploration_prob: 0.1
+  halt_max_steps: 16
+  hidden_size: 512
+  loss:
+    loss_type: stablemax_cross_entropy
+    name: losses@ACTLossHead
+  mlp_t: false
+  name: recursive_reasoning.glps@GLPS_ACTV1
+  num_heads: 8
+  pos_encodings: rope
+  puzzle_emb_ndim: 0
+  rms_norm_eps: 1.0e-05
+  rope_theta: 10000.0
+beta1: 0.9
+beta2: 0.95
+checkpoint_every_eval: true
+checkpoint_path: checkpoints/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_decay_ft10k
+data_paths:
+- data/sudoku-extreme-1k-aug-1000
+data_paths_test: []
+ema: true
+ema_rate: 0.999
+epochs: 50000
+eval_interval: 5000
+eval_save_outputs: []
+evaluators: []
+freeze_weights: false
+global_batch_size: 768
+load_checkpoint: checkpoints/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_decay/step_52080
+lr: 0.0001
+lr_min_ratio: 0.01
+lr_warmup_steps: 2000
+min_eval_interval: 0
+project_name: Sudoku-extreme-1k-aug-1000-ACT-torch
+puzzle_emb_lr: 0.0001
+puzzle_emb_weight_decay: 1.0
+run_name: pretrain_glps_sudoku_v4_decay_ft10k
+seed: 0
+weight_decay: 1.0
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_decay_ft10k/glps.py b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_decay_ft10k/glps.py
new file mode 100644
index 0000000000000000000000000000000000000000..96e6de932e79e174cf773779293a643842e8a268
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_decay_ft10k/glps.py
@@ -0,0 +1,409 @@
+
+from typing import Tuple, List, Dict
+from dataclasses import dataclass
+import math
+import torch
+import torch.nn.functional as F
+from torch import nn
+from pydantic import BaseModel
+
+# Reuse the same building blocks as HRM/TRM
+from models.common import trunc_normal_init_
+from models.layers import rms_norm, SwiGLU, Attention, RotaryEmbedding, CosSin, CastedEmbedding, CastedLinear
+from models.sparse_embedding import CastedSparseEmbedding
+
+"""
+Global-Local Predictive Solver (GLPS)
+------------------------------------
+A light-weight control-policy on top of the HRM/TRM style blocks:
+- H1: global scan -> certainty map
+- L1: fill-obvious (lock stable cells)
+- H2: dependency scoring over remaining cells
+- L2: targeted refinement (masked updates)
+- H3: energy-based confidence -> (optional) one global propagate sweep -> halt
+
+This file keeps parameter growth tiny: a few heads + (optional) low-rank dependency scorer.
+"""
+
+@dataclass
+class GLPS_ACTV1InnerCarry:
+    z_H: torch.Tensor
+    z_L: torch.Tensor
+
+@dataclass
+class GLPS_ACTV1Carry:
+    inner_carry: GLPS_ACTV1InnerCarry
+    steps: torch.Tensor
+    halted: torch.Tensor
+    current_data: Dict[str, torch.Tensor]
+
+class GLPS_ACTV1Config(BaseModel):
+    # Core IO / shapes
+    batch_size: int
+    seq_len: int
+    puzzle_emb_ndim: int = 0
+    num_puzzle_identifiers: int = 1
+    vocab_size: int = 256
+
+    # Cycle schedule
+    H_cycles: int = 3              # (scan -> refine -> check) typical
+    L_cycles: int = 1
+
+    # Depth
+    H_layers: int = 2
+    L_layers: int = 4
+
+    # Transformer config
+    hidden_size: int = 512
+    expansion: float = 2.0
+    num_heads: int = 8
+    pos_encodings: str = "rope"
+
+    rms_norm_eps: float = 1e-5
+    rope_theta: float = 10000.0
+
+    # ACT wrapper
+    halt_max_steps: int = 4
+    halt_exploration_prob: float = 0.1
+
+    forward_dtype: str = "bfloat16"
+
+    # Optional: use MLP on L instead of attention (matches HRM/TRM option)
+    mlp_t: bool = False
+
+    # ---- GLPS extras (tiny) ----
+    glps_enabled: bool = True
+    glps_fill_obvious: bool = True
+    glps_dep_graph: bool = True
+    glps_token_masking: bool = True
+    glps_global_propagate_on_low_conf: bool = True
+
+    glps_tau_halt: float = 0.95       # final confidence to halt
+    glps_tau_uncertain: float = 0.60  # cell-wise certainty threshold
+    glps_max_targeted_iters: int = 2  # small number: 1-2
+
+    # Dependency scorer (low rank bilinear)
+    dep_rank: int = 32
+    dep_topk: int = 8
+
+class GLPSBlock(nn.Module):
+    def __init__(self, config: GLPS_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        if self.config.mlp_t:
+            self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size)
+            self.mlp_t = SwiGLU(
+                hidden_size=self.config.seq_len + self.puzzle_emb_len, # treat sequence as channel
+                expansion=config.expansion,
+            )
+        else:
+            self.self_attn = Attention(
+                hidden_size=config.hidden_size,
+                head_dim=config.hidden_size // config.num_heads,
+                num_heads=config.num_heads,
+                num_key_value_heads=config.num_heads,
+                causal=False,
+            )
+        self.mlp = SwiGLU(hidden_size=config.hidden_size, expansion=config.expansion)
+        self.norm_eps = config.rms_norm_eps
+
+    def forward(self, cos_sin: CosSin, hidden_states: torch.Tensor) -> torch.Tensor:
+        if self.config.mlp_t:
+            # MLP over sequence dimension (mlp-t)
+            hidden_states = hidden_states.transpose(1, 2)
+            out = self.mlp_t(hidden_states)
+            hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+            hidden_states = hidden_states.transpose(1, 2)
+        else:
+            hidden_states = rms_norm(
+                hidden_states + self.self_attn(cos_sin=cos_sin, hidden_states=hidden_states),
+                variance_epsilon=self.norm_eps,
+            )
+        out = self.mlp(hidden_states)
+        hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+        return hidden_states
+
+class GLPSReasoningModule(nn.Module):
+    """Reasoning stack with optional masked updates (only update uncertain tokens)."""
+    def __init__(self, layers: List[GLPSBlock]):
+        super().__init__()
+        self.layers = torch.nn.ModuleList(layers)
+
+    def forward(self, hidden_states: torch.Tensor, input_injection: torch.Tensor, update_mask: torch.Tensor = None, **kwargs) -> torch.Tensor:
+        x = hidden_states
+        for layer in self.layers:
+            # Compute candidate update using injected context
+            y = layer(hidden_states=x + input_injection, **kwargs)
+            if update_mask is not None:
+                # Convex blend keeps frozen tokens unchanged
+                m = update_mask.to(x.dtype)[..., None]
+                x = x + m * (y - x)
+            else:
+                x = y
+        return x
+
+class GLPS_ACTV1_Inner(nn.Module):
+    def __init__(self, config: GLPS_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        self.forward_dtype = getattr(torch, self.config.forward_dtype)
+
+        # I/O
+        self.embed_scale  = math.sqrt(self.config.hidden_size)
+        embed_init_std = 1.0 / self.embed_scale
+
+        self.embed_tokens = CastedEmbedding(self.config.vocab_size, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+        self.lm_head      = CastedLinear(self.config.hidden_size, self.config.vocab_size, bias=False)
+        self.q_head       = CastedLinear(self.config.hidden_size, 2, bias=True)
+
+        # Puzzle emb (optional) — same convention as HRM/TRM
+        self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size)  # ceil div
+        if self.config.puzzle_emb_ndim > 0:
+            self.puzzle_emb = CastedSparseEmbedding(self.config.num_puzzle_identifiers, self.config.puzzle_emb_ndim, batch_size=self.config.batch_size, init_std=0, cast_to=self.forward_dtype)
+
+        # Positional encodings
+        if self.config.pos_encodings == "rope":
+            self.rotary_emb = RotaryEmbedding(dim=self.config.hidden_size // self.config.num_heads, max_position_embeddings=self.config.seq_len + self.puzzle_emb_len, base=self.config.rope_theta)
+        elif self.config.pos_encodings == "learned":
+            self.embed_pos = CastedEmbedding(self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+
+        # Reasoning stacks
+        self.H_level = GLPSReasoningModule(layers=[GLPSBlock(self.config) for _ in range(self.config.H_layers)])
+        self.L_level = GLPSReasoningModule(layers=[GLPSBlock(self.config) for _ in range(self.config.L_layers)])
+
+        # Initial states (match HRM/TRM style)
+        H_init = trunc_normal_init_(torch.empty(1, 1, self.config.hidden_size, dtype=self.forward_dtype), std=1.0)
+        L_init = trunc_normal_init_(torch.empty(1, 1, self.config.hidden_size, dtype=self.forward_dtype), std=1.0)
+        self.register_buffer("H_init", H_init, persistent=True)
+        self.register_buffer("L_init", L_init, persistent=True)
+
+        # GLPS small heads
+        self.candidate_head = CastedLinear(self.config.hidden_size, 16, bias=True)  # task-specific; for Sudoku you can slice to 9
+        self.certainty_head = CastedLinear(self.config.hidden_size, 1, bias=True)
+        self.energy_head    = CastedLinear(self.config.hidden_size, 1, bias=True)
+
+        # Low-rank dependency scorer (shared)
+        r = max(1, self.config.dep_rank)
+        self.dep_q = CastedLinear(self.config.hidden_size, r, bias=False)
+        self.dep_k = CastedLinear(self.config.hidden_size, r, bias=False)
+
+        # Q head init like HRM/TRM (near-zero -> easier bootstrapping)
+        with torch.no_grad():
+            self.q_head.weight.zero_()
+            self.q_head.bias.fill_(-5)
+
+    def _input_embeddings(self, input: torch.Tensor, puzzle_identifiers: torch.Tensor):
+        # Token embedding
+        embedding = self.embed_tokens(input.to(torch.int32))
+
+        # Puzzle embeddings
+        if self.config.puzzle_emb_ndim > 0:
+            puzzle_embedding = self.puzzle_emb(puzzle_identifiers)
+            pad_count = self.puzzle_emb_len * self.config.hidden_size - puzzle_embedding.shape[-1]
+            if pad_count > 0:
+                puzzle_embedding = F.pad(puzzle_embedding, (0, pad_count))
+            embedding = torch.cat((puzzle_embedding.view(-1, self.puzzle_emb_len, self.config.hidden_size), embedding), dim=-2)
+
+        # Position embeddings
+        if self.config.pos_encodings == "learned":
+            embedding = 0.707106781 * (embedding + self.embed_pos.embedding_weight.to(self.forward_dtype))
+
+        return self.embed_scale * embedding
+
+    def empty_carry(self, batch_size: int):
+        return GLPS_ACTV1InnerCarry(
+            z_H=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+            z_L=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+        )
+
+    def reset_carry(self, reset_flag: torch.Tensor, carry: GLPS_ACTV1InnerCarry):
+        # Explicitly expand buffers and mask to target shapes to avoid shape confusion
+        B, L, D = carry.z_H.shape
+        # Reduce/reset flag to per-batch boolean vector of shape [B]
+        if reset_flag.ndim == 1 and reset_flag.shape[0] == B:
+            reset_b = reset_flag.to(torch.bool)
+        else:
+            # If shape is [B, ...] reduce across non-batch dims; otherwise fallback to first B entries
+            try:
+                reset_b = reset_flag.reshape(B, -1).any(dim=1).to(torch.bool)
+            except Exception:
+                reset_b = reset_flag.reshape(-1)[:B].to(torch.bool)
+        m = reset_b.view(B, 1, 1)
+        mH = m.expand(B, L, D)
+        mL = mH  # same shape for z_L
+        H_init_exp = self.H_init.expand(B, L, D)
+        L_init_exp = self.L_init.expand(B, L, D)
+        return GLPS_ACTV1InnerCarry(
+            z_H=torch.where(mH, H_init_exp, carry.z_H),
+            z_L=torch.where(mL, L_init_exp, carry.z_L),
+        )
+
+    def _global_scan(self, z_L, z_H, input_embeddings, seq_info):
+        # One light pass to gather global signals
+        z_scan = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+        cand_logits = self.candidate_head(z_scan)              # [B, L, C]
+        certainty   = torch.sigmoid(self.certainty_head(z_scan))  # [B, L, 1]
+        return z_scan, cand_logits, certainty
+
+    def _build_dep_mask(self, z_ctx, uncertain_mask: torch.Tensor):
+        """Compute a dependency-based focus mask from a low-rank bilinear score.
+        uncertain_mask: [B, L] boolean mask of cells that are currently uncertain
+        Returns: dep_mask [B, L] boolean mask of cells to (re)update.
+        """
+        B, L, D = z_ctx.shape
+        # Project to low-rank space; use float32 to avoid dtype mismatches under torch.compile
+        Q = self.dep_q(z_ctx).to(torch.float32)   # [B, L, r]
+        K = self.dep_k(z_ctx).to(torch.float32)   # [B, L, r]
+        r = max(1, int(Q.shape[-1]))
+        sim = torch.matmul(Q, K.transpose(1, 2))  # [B, L, L] (float32)
+        sim = sim / math.sqrt(r)
+
+        # Aggregate influence from uncertain queries onto target tokens
+        src = uncertain_mask.to(sim.dtype).unsqueeze(1)      # [B, 1, L]
+        influence = torch.matmul(src, sim).squeeze(1)        # [B, L]
+
+        # Top-k influenced tokens per batch
+        topk = min(self.config.dep_topk, L)
+        vals, idx = torch.topk(influence, k=topk, dim=-1)    # [B, topk]
+        dep_mask = torch.zeros_like(uncertain_mask)
+        dep_mask.scatter_(1, idx, True)
+
+        # Always include uncertain cells themselves
+        dep_mask = dep_mask | uncertain_mask
+        return dep_mask
+
+    def forward(self, carry: GLPS_ACTV1InnerCarry, batch: Dict[str, torch.Tensor]):
+        seq_info = dict(cos_sin=getattr(self, "rotary_emb", None)() if hasattr(self, "rotary_emb") else None)
+
+        # Encode inputs
+        input_embeddings = self._input_embeddings(batch["inputs"], batch["puzzle_identifiers"])
+
+        # States
+        z_H, z_L = carry.z_H, carry.z_L
+
+        if not self.config.glps_enabled:
+            # Fallback to an HRM-like single-cycle grad update for compatibility
+            with torch.no_grad():
+                for _H in range(self.config.H_cycles - 1):
+                    for _L in range(self.config.L_cycles):
+                        z_L = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+                    z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+            # final grad step
+            for _L in range(self.config.L_cycles):
+                z_L = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+            z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+
+            # Outputs
+            new_carry = GLPS_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach())
+            logits = self.lm_head(z_H)[:, self.puzzle_emb_len:]
+            q_logits = self.q_head(z_H[:, 0]).to(torch.float32)
+            conf = torch.zeros_like(q_logits[..., :1]) + 0.5  # neutral
+            return new_carry, logits, (q_logits[..., 0], q_logits[..., 1]), conf
+
+        # ===== GLPS path =====
+        # H1: global scan (cheap)
+        with torch.no_grad():
+            z_scan, cand_logits, certainty = self._global_scan(z_L, z_H, input_embeddings, seq_info)
+
+        # L1: fill-obvious -> compute stable vs uncertain masks
+        if self.config.glps_fill_obvious:
+            obvious_mask = (certainty >= self.config.glps_tau_uncertain)  # [B, L, 1]
+        else:
+            obvious_mask = torch.zeros_like(certainty).bool()
+        stable_mask = obvious_mask.squeeze(-1)         # [B, L]
+        uncertain_mask = ~stable_mask                  # [B, L]
+
+        # H2: dependency prediction over remaining cells
+        if self.config.glps_dep_graph:
+            dep_mask = self._build_dep_mask(z_scan, uncertain_mask)   # [B, L]
+        else:
+            dep_mask = uncertain_mask
+
+        # L2: targeted refinement (a couple of masked iters)
+        update_mask = dep_mask if self.config.glps_token_masking else None
+        z = z_scan.detach()  # use scanned context as start
+        for _ in range(self.config.glps_max_targeted_iters):
+            z = self.L_level(z, z_H + input_embeddings, update_mask=update_mask, **seq_info)
+            # Refresh certainty to shrink mask (optional but cheap)
+            cert_now = torch.sigmoid(self.certainty_head(z)).squeeze(-1)
+            if self.config.glps_token_masking:
+                update_mask = dep_mask & (cert_now < self.config.glps_tau_uncertain)
+
+        # Merge into H and do a light H update with grad
+        z_L = z
+        z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+
+        # H3: energy/consistency -> confidence & optional global propagate
+        energy = torch.sigmoid(self.energy_head(z_H)).mean(dim=1)  # [B, 1]
+        conf = 1.0 - energy
+        need_sweep = (conf.squeeze(-1) < self.config.glps_tau_halt)
+        if self.config.glps_global_propagate_on_low_conf and need_sweep.any():
+            # one final full sweep only for rows needing it
+            maskB = need_sweep.view(-1, 1, 1)
+            zL2 = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+            zH2 = self.H_level(z_H, zL2, update_mask=None, **seq_info)
+            z_L = torch.where(maskB, zL2, z_L)
+            z_H = torch.where(maskB, zH2, z_H)
+
+        # Outputs
+        new_carry = GLPS_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach())
+        logits = self.lm_head(z_H)[:, self.puzzle_emb_len:]
+        q_logits = self.q_head(z_H[:, 0]).to(torch.float32)
+        return new_carry, logits, (q_logits[..., 0], q_logits[..., 1]), conf
+
+class GLPS_ACTV1(nn.Module):
+    """ACT-style wrapper that mixes Q-halt with GLPS confidence."""
+    def __init__(self, config_dict: dict):
+        super().__init__()
+        self.config = GLPS_ACTV1Config(**config_dict)
+        self.inner = GLPS_ACTV1_Inner(self.config)
+
+    @property
+    def puzzle_emb(self):
+        return self.inner.puzzle_emb
+
+    def initial_carry(self, batch: Dict[str, torch.Tensor]):
+        batch_size = batch["inputs"].shape[0]
+        return GLPS_ACTV1Carry(
+            inner_carry=self.inner.empty_carry(batch_size),
+            steps=torch.zeros((batch_size,), dtype=torch.int32),
+            halted=torch.ones((batch_size,), dtype=torch.bool),  # start halted to force reset on first pass
+            current_data={k: torch.empty_like(v) for k, v in batch.items()}
+        )
+
+    def forward(self, carry: GLPS_ACTV1Carry, batch: Dict[str, torch.Tensor]):
+        # Reset halted seqs
+        new_inner_carry = self.inner.reset_carry(carry.halted, carry.inner_carry)
+        new_steps = torch.where(carry.halted, 0, carry.steps)
+        new_current_data = {k: torch.where(carry.halted.view((-1,) + (1,) * (batch[k].ndim - 1)), batch[k], v) for k, v in carry.current_data.items()}
+
+        # Inner step
+        new_inner_carry, logits, (q_halt_logits, q_continue_logits), conf = self.inner(new_inner_carry, new_current_data)
+
+        outputs = {
+            "logits": logits,
+            "q_halt_logits": q_halt_logits,
+            "q_continue_logits": q_continue_logits,
+            "conf": conf.squeeze(-1),
+        }
+
+        with torch.no_grad():
+            new_steps = new_steps + 1
+            is_last_step = new_steps >= self.config.halt_max_steps
+
+            # Combine halt signals: Q or confidence or last-step
+            halted = is_last_step | (q_halt_logits > q_continue_logits) | (conf.squeeze(-1) >= self.config.glps_tau_halt)
+
+            # Exploration during training only
+            if self.training and (self.config.halt_max_steps > 1):
+                min_halt_steps = (torch.rand_like(q_halt_logits) < self.config.halt_exploration_prob) * torch.randint_like(new_steps, low=2, high=self.config.halt_max_steps + 1)
+                halted = halted & (new_steps >= min_halt_steps)
+                
+                # Optional target for Q-learning (kept similar to HRM)
+                next_conf = self.inner(new_inner_carry, new_current_data)[-1]
+                outputs["target_q_continue"] = torch.sigmoid(torch.where(is_last_step, q_halt_logits, torch.maximum(q_halt_logits, q_continue_logits)))
+            else:
+                # During eval, always use max_steps to ensure consistent reasoning depth (same as TRM/HRM eval behavior)
+                halted = is_last_step
+
+        return GLPS_ACTV1Carry(new_inner_carry, new_steps, halted, new_current_data), outputs
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_decay_ft10k/losses.py b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_decay_ft10k/losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..cdfb77718bc16f98cdd7da06eb5795c36b866aa7
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_decay_ft10k/losses.py
@@ -0,0 +1,102 @@
+from typing import Any, Tuple, Dict, Sequence, Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+import math
+
+IGNORE_LABEL_ID = -100
+
+
+def s(x, epsilon=1e-30):
+    return torch.where(
+        x<0,
+        1/(1-x+ epsilon),
+        x + 1
+    )
+
+
+def log_stablemax(x, dim=-1):
+    s_x = s(x)
+    return torch.log(s_x/torch.sum(s_x, dim=dim, keepdim=True))
+
+
+def stablemax_cross_entropy(logits, labels, ignore_index: int = -100, valid_mask=None):
+    logprobs = log_stablemax(logits.to(torch.float64), dim=-1)
+
+    if valid_mask is None:
+        valid_mask = (labels != ignore_index)
+    transformed_labels = torch.where(valid_mask, labels, 0)
+    prediction_logprobs = torch.gather(logprobs, index=transformed_labels.to(torch.long).unsqueeze(-1), dim=-1).squeeze(-1)
+
+    return -torch.where(valid_mask, prediction_logprobs, 0)
+
+
+def softmax_cross_entropy(logits, labels, ignore_index: int = -100):
+    # Cast logits to f32
+    # Flatten logits
+    return F.cross_entropy(logits.to(torch.float32).view(-1, logits.shape[-1]), labels.to(torch.long).view(-1), ignore_index=ignore_index, reduction="none").view(labels.shape)
+
+
+class ACTLossHead(nn.Module):
+    def __init__(self, model: nn.Module, loss_type: str):
+        super().__init__()
+        self.model = model
+        self.loss_fn = globals()[loss_type]
+        
+    def initial_carry(self, *args, **kwargs):
+        return self.model.initial_carry(*args, **kwargs)  # type: ignore
+
+    def forward(
+        self,
+        return_keys: Sequence[str],
+        # Model args
+        **model_kwargs,
+    ) -> Tuple[Any, torch.Tensor, Dict[str, torch.Tensor], Optional[Dict[str, torch.Tensor]], torch.Tensor]:
+        # Model logits
+        # B x SeqLen x D
+        new_carry, outputs = self.model(**model_kwargs)
+        labels = new_carry.current_data["labels"]
+
+        with torch.no_grad():
+            # Preds
+            outputs["preds"] = torch.argmax(outputs["logits"], dim=-1)
+
+            # Correctness
+            mask = (labels != IGNORE_LABEL_ID)
+            loss_counts = mask.sum(-1)
+            loss_divisor = loss_counts.clamp_min(1).unsqueeze(-1)  # Avoid NaNs in division
+
+            is_correct = mask & (torch.argmax(outputs["logits"], dim=-1) == labels)
+            seq_is_correct = is_correct.sum(-1) == loss_counts
+            
+            # Metrics (halted)
+            valid_metrics = new_carry.halted & (loss_counts > 0)
+            metrics = {
+                "count": valid_metrics.sum(),
+                
+                "accuracy":       torch.where(valid_metrics, (is_correct.to(torch.float32) / loss_divisor).sum(-1), 0).sum(),
+                "exact_accuracy": (valid_metrics & seq_is_correct).sum(),
+
+                "q_halt_accuracy": (valid_metrics & ((outputs["q_halt_logits"] >= 0) == seq_is_correct)).sum(),
+                "steps":          torch.where(valid_metrics, new_carry.steps, 0).sum(),
+            }
+
+        # Losses
+
+        lm_loss = (self.loss_fn(outputs["logits"], labels, ignore_index=IGNORE_LABEL_ID, valid_mask=mask) / loss_divisor).sum()
+        q_halt_loss = F.binary_cross_entropy_with_logits(outputs["q_halt_logits"], seq_is_correct.to(outputs["q_halt_logits"].dtype), reduction="sum")
+        metrics.update({
+            "lm_loss": lm_loss.detach(),
+            "q_halt_loss": q_halt_loss.detach(),
+        })
+        # Q continue (bootstrapping target loss); Alexia: This fits Q-learning, but seems totally unecessary
+        q_continue_loss = 0
+        if "target_q_continue" in outputs:
+            q_continue_loss = F.binary_cross_entropy_with_logits(outputs["q_continue_logits"], outputs["target_q_continue"], reduction="sum")
+
+            metrics["q_continue_loss"] = q_continue_loss.detach()
+        # Filter outputs for return
+        detached_outputs = {k: outputs[k].detach() for k in return_keys if k in outputs}
+
+        return new_carry, lm_loss + 0.5 * (q_halt_loss + q_continue_loss), metrics, detached_outputs, new_carry.halted.all()
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_evalboost/all_config.yaml b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_evalboost/all_config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..202526f3dbbd21822ce1c10c183d643655e75788
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_evalboost/all_config.yaml
@@ -0,0 +1,60 @@
+arch:
+  H_cycles: 3
+  H_layers: 2
+  L_cycles: 1
+  L_layers: 7
+  dep_rank: 64
+  dep_topk: 12
+  expansion: 4.0
+  forward_dtype: bfloat16
+  glps_dep_graph: true
+  glps_enabled: true
+  glps_fill_obvious: true
+  glps_global_propagate_on_low_conf: true
+  glps_max_targeted_iters: 4
+  glps_tau_halt: 0.95
+  glps_tau_uncertain: 0.8
+  glps_token_masking: true
+  halt_exploration_prob: 0.1
+  halt_max_steps: 16
+  hidden_size: 512
+  loss:
+    loss_type: stablemax_cross_entropy
+    name: losses@ACTLossHead
+  mlp_t: false
+  name: recursive_reasoning.glps@GLPS_ACTV1
+  num_heads: 8
+  pos_encodings: rope
+  puzzle_emb_ndim: 0
+  rms_norm_eps: 1.0e-05
+  rope_theta: 10000.0
+beta1: 0.9
+beta2: 0.95
+checkpoint_every_eval: true
+checkpoint_path: checkpoints/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_evalboost
+data_paths:
+- data/sudoku-extreme-1k-aug-1000
+data_paths_test: []
+ema: true
+ema_rate: 0.999
+epochs: 50000
+eval_glps_max_targeted_iters: 6
+eval_glps_tau_halt: 0.85
+eval_halt_max_steps: 32
+eval_interval: 5000
+eval_only: true
+eval_save_outputs: []
+evaluators: []
+freeze_weights: false
+global_batch_size: 768
+load_checkpoint: /teamspace/studios/this_studio/TinyRecursiveModels/checkpoints/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_decay_ft10k/step_58590
+lr: 0.0001
+lr_min_ratio: 0.01
+lr_warmup_steps: 2000
+min_eval_interval: 0
+project_name: Sudoku-extreme-1k-aug-1000-ACT-torch
+puzzle_emb_lr: 0.0001
+puzzle_emb_weight_decay: 1.0
+run_name: pretrain_glps_sudoku_v4_evalboost
+seed: 0
+weight_decay: 1.0
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_evalboost/glps.py b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_evalboost/glps.py
new file mode 100644
index 0000000000000000000000000000000000000000..96e6de932e79e174cf773779293a643842e8a268
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_evalboost/glps.py
@@ -0,0 +1,409 @@
+
+from typing import Tuple, List, Dict
+from dataclasses import dataclass
+import math
+import torch
+import torch.nn.functional as F
+from torch import nn
+from pydantic import BaseModel
+
+# Reuse the same building blocks as HRM/TRM
+from models.common import trunc_normal_init_
+from models.layers import rms_norm, SwiGLU, Attention, RotaryEmbedding, CosSin, CastedEmbedding, CastedLinear
+from models.sparse_embedding import CastedSparseEmbedding
+
+"""
+Global-Local Predictive Solver (GLPS)
+------------------------------------
+A light-weight control-policy on top of the HRM/TRM style blocks:
+- H1: global scan -> certainty map
+- L1: fill-obvious (lock stable cells)
+- H2: dependency scoring over remaining cells
+- L2: targeted refinement (masked updates)
+- H3: energy-based confidence -> (optional) one global propagate sweep -> halt
+
+This file keeps parameter growth tiny: a few heads + (optional) low-rank dependency scorer.
+"""
+
+@dataclass
+class GLPS_ACTV1InnerCarry:
+    z_H: torch.Tensor
+    z_L: torch.Tensor
+
+@dataclass
+class GLPS_ACTV1Carry:
+    inner_carry: GLPS_ACTV1InnerCarry
+    steps: torch.Tensor
+    halted: torch.Tensor
+    current_data: Dict[str, torch.Tensor]
+
+class GLPS_ACTV1Config(BaseModel):
+    # Core IO / shapes
+    batch_size: int
+    seq_len: int
+    puzzle_emb_ndim: int = 0
+    num_puzzle_identifiers: int = 1
+    vocab_size: int = 256
+
+    # Cycle schedule
+    H_cycles: int = 3              # (scan -> refine -> check) typical
+    L_cycles: int = 1
+
+    # Depth
+    H_layers: int = 2
+    L_layers: int = 4
+
+    # Transformer config
+    hidden_size: int = 512
+    expansion: float = 2.0
+    num_heads: int = 8
+    pos_encodings: str = "rope"
+
+    rms_norm_eps: float = 1e-5
+    rope_theta: float = 10000.0
+
+    # ACT wrapper
+    halt_max_steps: int = 4
+    halt_exploration_prob: float = 0.1
+
+    forward_dtype: str = "bfloat16"
+
+    # Optional: use MLP on L instead of attention (matches HRM/TRM option)
+    mlp_t: bool = False
+
+    # ---- GLPS extras (tiny) ----
+    glps_enabled: bool = True
+    glps_fill_obvious: bool = True
+    glps_dep_graph: bool = True
+    glps_token_masking: bool = True
+    glps_global_propagate_on_low_conf: bool = True
+
+    glps_tau_halt: float = 0.95       # final confidence to halt
+    glps_tau_uncertain: float = 0.60  # cell-wise certainty threshold
+    glps_max_targeted_iters: int = 2  # small number: 1-2
+
+    # Dependency scorer (low rank bilinear)
+    dep_rank: int = 32
+    dep_topk: int = 8
+
+class GLPSBlock(nn.Module):
+    def __init__(self, config: GLPS_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        if self.config.mlp_t:
+            self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size)
+            self.mlp_t = SwiGLU(
+                hidden_size=self.config.seq_len + self.puzzle_emb_len, # treat sequence as channel
+                expansion=config.expansion,
+            )
+        else:
+            self.self_attn = Attention(
+                hidden_size=config.hidden_size,
+                head_dim=config.hidden_size // config.num_heads,
+                num_heads=config.num_heads,
+                num_key_value_heads=config.num_heads,
+                causal=False,
+            )
+        self.mlp = SwiGLU(hidden_size=config.hidden_size, expansion=config.expansion)
+        self.norm_eps = config.rms_norm_eps
+
+    def forward(self, cos_sin: CosSin, hidden_states: torch.Tensor) -> torch.Tensor:
+        if self.config.mlp_t:
+            # MLP over sequence dimension (mlp-t)
+            hidden_states = hidden_states.transpose(1, 2)
+            out = self.mlp_t(hidden_states)
+            hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+            hidden_states = hidden_states.transpose(1, 2)
+        else:
+            hidden_states = rms_norm(
+                hidden_states + self.self_attn(cos_sin=cos_sin, hidden_states=hidden_states),
+                variance_epsilon=self.norm_eps,
+            )
+        out = self.mlp(hidden_states)
+        hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+        return hidden_states
+
+class GLPSReasoningModule(nn.Module):
+    """Reasoning stack with optional masked updates (only update uncertain tokens)."""
+    def __init__(self, layers: List[GLPSBlock]):
+        super().__init__()
+        self.layers = torch.nn.ModuleList(layers)
+
+    def forward(self, hidden_states: torch.Tensor, input_injection: torch.Tensor, update_mask: torch.Tensor = None, **kwargs) -> torch.Tensor:
+        x = hidden_states
+        for layer in self.layers:
+            # Compute candidate update using injected context
+            y = layer(hidden_states=x + input_injection, **kwargs)
+            if update_mask is not None:
+                # Convex blend keeps frozen tokens unchanged
+                m = update_mask.to(x.dtype)[..., None]
+                x = x + m * (y - x)
+            else:
+                x = y
+        return x
+
+class GLPS_ACTV1_Inner(nn.Module):
+    def __init__(self, config: GLPS_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        self.forward_dtype = getattr(torch, self.config.forward_dtype)
+
+        # I/O
+        self.embed_scale  = math.sqrt(self.config.hidden_size)
+        embed_init_std = 1.0 / self.embed_scale
+
+        self.embed_tokens = CastedEmbedding(self.config.vocab_size, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+        self.lm_head      = CastedLinear(self.config.hidden_size, self.config.vocab_size, bias=False)
+        self.q_head       = CastedLinear(self.config.hidden_size, 2, bias=True)
+
+        # Puzzle emb (optional) — same convention as HRM/TRM
+        self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size)  # ceil div
+        if self.config.puzzle_emb_ndim > 0:
+            self.puzzle_emb = CastedSparseEmbedding(self.config.num_puzzle_identifiers, self.config.puzzle_emb_ndim, batch_size=self.config.batch_size, init_std=0, cast_to=self.forward_dtype)
+
+        # Positional encodings
+        if self.config.pos_encodings == "rope":
+            self.rotary_emb = RotaryEmbedding(dim=self.config.hidden_size // self.config.num_heads, max_position_embeddings=self.config.seq_len + self.puzzle_emb_len, base=self.config.rope_theta)
+        elif self.config.pos_encodings == "learned":
+            self.embed_pos = CastedEmbedding(self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+
+        # Reasoning stacks
+        self.H_level = GLPSReasoningModule(layers=[GLPSBlock(self.config) for _ in range(self.config.H_layers)])
+        self.L_level = GLPSReasoningModule(layers=[GLPSBlock(self.config) for _ in range(self.config.L_layers)])
+
+        # Initial states (match HRM/TRM style)
+        H_init = trunc_normal_init_(torch.empty(1, 1, self.config.hidden_size, dtype=self.forward_dtype), std=1.0)
+        L_init = trunc_normal_init_(torch.empty(1, 1, self.config.hidden_size, dtype=self.forward_dtype), std=1.0)
+        self.register_buffer("H_init", H_init, persistent=True)
+        self.register_buffer("L_init", L_init, persistent=True)
+
+        # GLPS small heads
+        self.candidate_head = CastedLinear(self.config.hidden_size, 16, bias=True)  # task-specific; for Sudoku you can slice to 9
+        self.certainty_head = CastedLinear(self.config.hidden_size, 1, bias=True)
+        self.energy_head    = CastedLinear(self.config.hidden_size, 1, bias=True)
+
+        # Low-rank dependency scorer (shared)
+        r = max(1, self.config.dep_rank)
+        self.dep_q = CastedLinear(self.config.hidden_size, r, bias=False)
+        self.dep_k = CastedLinear(self.config.hidden_size, r, bias=False)
+
+        # Q head init like HRM/TRM (near-zero -> easier bootstrapping)
+        with torch.no_grad():
+            self.q_head.weight.zero_()
+            self.q_head.bias.fill_(-5)
+
+    def _input_embeddings(self, input: torch.Tensor, puzzle_identifiers: torch.Tensor):
+        # Token embedding
+        embedding = self.embed_tokens(input.to(torch.int32))
+
+        # Puzzle embeddings
+        if self.config.puzzle_emb_ndim > 0:
+            puzzle_embedding = self.puzzle_emb(puzzle_identifiers)
+            pad_count = self.puzzle_emb_len * self.config.hidden_size - puzzle_embedding.shape[-1]
+            if pad_count > 0:
+                puzzle_embedding = F.pad(puzzle_embedding, (0, pad_count))
+            embedding = torch.cat((puzzle_embedding.view(-1, self.puzzle_emb_len, self.config.hidden_size), embedding), dim=-2)
+
+        # Position embeddings
+        if self.config.pos_encodings == "learned":
+            embedding = 0.707106781 * (embedding + self.embed_pos.embedding_weight.to(self.forward_dtype))
+
+        return self.embed_scale * embedding
+
+    def empty_carry(self, batch_size: int):
+        return GLPS_ACTV1InnerCarry(
+            z_H=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+            z_L=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+        )
+
+    def reset_carry(self, reset_flag: torch.Tensor, carry: GLPS_ACTV1InnerCarry):
+        # Explicitly expand buffers and mask to target shapes to avoid shape confusion
+        B, L, D = carry.z_H.shape
+        # Reduce/reset flag to per-batch boolean vector of shape [B]
+        if reset_flag.ndim == 1 and reset_flag.shape[0] == B:
+            reset_b = reset_flag.to(torch.bool)
+        else:
+            # If shape is [B, ...] reduce across non-batch dims; otherwise fallback to first B entries
+            try:
+                reset_b = reset_flag.reshape(B, -1).any(dim=1).to(torch.bool)
+            except Exception:
+                reset_b = reset_flag.reshape(-1)[:B].to(torch.bool)
+        m = reset_b.view(B, 1, 1)
+        mH = m.expand(B, L, D)
+        mL = mH  # same shape for z_L
+        H_init_exp = self.H_init.expand(B, L, D)
+        L_init_exp = self.L_init.expand(B, L, D)
+        return GLPS_ACTV1InnerCarry(
+            z_H=torch.where(mH, H_init_exp, carry.z_H),
+            z_L=torch.where(mL, L_init_exp, carry.z_L),
+        )
+
+    def _global_scan(self, z_L, z_H, input_embeddings, seq_info):
+        # One light pass to gather global signals
+        z_scan = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+        cand_logits = self.candidate_head(z_scan)              # [B, L, C]
+        certainty   = torch.sigmoid(self.certainty_head(z_scan))  # [B, L, 1]
+        return z_scan, cand_logits, certainty
+
+    def _build_dep_mask(self, z_ctx, uncertain_mask: torch.Tensor):
+        """Compute a dependency-based focus mask from a low-rank bilinear score.
+        uncertain_mask: [B, L] boolean mask of cells that are currently uncertain
+        Returns: dep_mask [B, L] boolean mask of cells to (re)update.
+        """
+        B, L, D = z_ctx.shape
+        # Project to low-rank space; use float32 to avoid dtype mismatches under torch.compile
+        Q = self.dep_q(z_ctx).to(torch.float32)   # [B, L, r]
+        K = self.dep_k(z_ctx).to(torch.float32)   # [B, L, r]
+        r = max(1, int(Q.shape[-1]))
+        sim = torch.matmul(Q, K.transpose(1, 2))  # [B, L, L] (float32)
+        sim = sim / math.sqrt(r)
+
+        # Aggregate influence from uncertain queries onto target tokens
+        src = uncertain_mask.to(sim.dtype).unsqueeze(1)      # [B, 1, L]
+        influence = torch.matmul(src, sim).squeeze(1)        # [B, L]
+
+        # Top-k influenced tokens per batch
+        topk = min(self.config.dep_topk, L)
+        vals, idx = torch.topk(influence, k=topk, dim=-1)    # [B, topk]
+        dep_mask = torch.zeros_like(uncertain_mask)
+        dep_mask.scatter_(1, idx, True)
+
+        # Always include uncertain cells themselves
+        dep_mask = dep_mask | uncertain_mask
+        return dep_mask
+
+    def forward(self, carry: GLPS_ACTV1InnerCarry, batch: Dict[str, torch.Tensor]):
+        seq_info = dict(cos_sin=getattr(self, "rotary_emb", None)() if hasattr(self, "rotary_emb") else None)
+
+        # Encode inputs
+        input_embeddings = self._input_embeddings(batch["inputs"], batch["puzzle_identifiers"])
+
+        # States
+        z_H, z_L = carry.z_H, carry.z_L
+
+        if not self.config.glps_enabled:
+            # Fallback to an HRM-like single-cycle grad update for compatibility
+            with torch.no_grad():
+                for _H in range(self.config.H_cycles - 1):
+                    for _L in range(self.config.L_cycles):
+                        z_L = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+                    z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+            # final grad step
+            for _L in range(self.config.L_cycles):
+                z_L = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+            z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+
+            # Outputs
+            new_carry = GLPS_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach())
+            logits = self.lm_head(z_H)[:, self.puzzle_emb_len:]
+            q_logits = self.q_head(z_H[:, 0]).to(torch.float32)
+            conf = torch.zeros_like(q_logits[..., :1]) + 0.5  # neutral
+            return new_carry, logits, (q_logits[..., 0], q_logits[..., 1]), conf
+
+        # ===== GLPS path =====
+        # H1: global scan (cheap)
+        with torch.no_grad():
+            z_scan, cand_logits, certainty = self._global_scan(z_L, z_H, input_embeddings, seq_info)
+
+        # L1: fill-obvious -> compute stable vs uncertain masks
+        if self.config.glps_fill_obvious:
+            obvious_mask = (certainty >= self.config.glps_tau_uncertain)  # [B, L, 1]
+        else:
+            obvious_mask = torch.zeros_like(certainty).bool()
+        stable_mask = obvious_mask.squeeze(-1)         # [B, L]
+        uncertain_mask = ~stable_mask                  # [B, L]
+
+        # H2: dependency prediction over remaining cells
+        if self.config.glps_dep_graph:
+            dep_mask = self._build_dep_mask(z_scan, uncertain_mask)   # [B, L]
+        else:
+            dep_mask = uncertain_mask
+
+        # L2: targeted refinement (a couple of masked iters)
+        update_mask = dep_mask if self.config.glps_token_masking else None
+        z = z_scan.detach()  # use scanned context as start
+        for _ in range(self.config.glps_max_targeted_iters):
+            z = self.L_level(z, z_H + input_embeddings, update_mask=update_mask, **seq_info)
+            # Refresh certainty to shrink mask (optional but cheap)
+            cert_now = torch.sigmoid(self.certainty_head(z)).squeeze(-1)
+            if self.config.glps_token_masking:
+                update_mask = dep_mask & (cert_now < self.config.glps_tau_uncertain)
+
+        # Merge into H and do a light H update with grad
+        z_L = z
+        z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+
+        # H3: energy/consistency -> confidence & optional global propagate
+        energy = torch.sigmoid(self.energy_head(z_H)).mean(dim=1)  # [B, 1]
+        conf = 1.0 - energy
+        need_sweep = (conf.squeeze(-1) < self.config.glps_tau_halt)
+        if self.config.glps_global_propagate_on_low_conf and need_sweep.any():
+            # one final full sweep only for rows needing it
+            maskB = need_sweep.view(-1, 1, 1)
+            zL2 = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+            zH2 = self.H_level(z_H, zL2, update_mask=None, **seq_info)
+            z_L = torch.where(maskB, zL2, z_L)
+            z_H = torch.where(maskB, zH2, z_H)
+
+        # Outputs
+        new_carry = GLPS_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach())
+        logits = self.lm_head(z_H)[:, self.puzzle_emb_len:]
+        q_logits = self.q_head(z_H[:, 0]).to(torch.float32)
+        return new_carry, logits, (q_logits[..., 0], q_logits[..., 1]), conf
+
+class GLPS_ACTV1(nn.Module):
+    """ACT-style wrapper that mixes Q-halt with GLPS confidence."""
+    def __init__(self, config_dict: dict):
+        super().__init__()
+        self.config = GLPS_ACTV1Config(**config_dict)
+        self.inner = GLPS_ACTV1_Inner(self.config)
+
+    @property
+    def puzzle_emb(self):
+        return self.inner.puzzle_emb
+
+    def initial_carry(self, batch: Dict[str, torch.Tensor]):
+        batch_size = batch["inputs"].shape[0]
+        return GLPS_ACTV1Carry(
+            inner_carry=self.inner.empty_carry(batch_size),
+            steps=torch.zeros((batch_size,), dtype=torch.int32),
+            halted=torch.ones((batch_size,), dtype=torch.bool),  # start halted to force reset on first pass
+            current_data={k: torch.empty_like(v) for k, v in batch.items()}
+        )
+
+    def forward(self, carry: GLPS_ACTV1Carry, batch: Dict[str, torch.Tensor]):
+        # Reset halted seqs
+        new_inner_carry = self.inner.reset_carry(carry.halted, carry.inner_carry)
+        new_steps = torch.where(carry.halted, 0, carry.steps)
+        new_current_data = {k: torch.where(carry.halted.view((-1,) + (1,) * (batch[k].ndim - 1)), batch[k], v) for k, v in carry.current_data.items()}
+
+        # Inner step
+        new_inner_carry, logits, (q_halt_logits, q_continue_logits), conf = self.inner(new_inner_carry, new_current_data)
+
+        outputs = {
+            "logits": logits,
+            "q_halt_logits": q_halt_logits,
+            "q_continue_logits": q_continue_logits,
+            "conf": conf.squeeze(-1),
+        }
+
+        with torch.no_grad():
+            new_steps = new_steps + 1
+            is_last_step = new_steps >= self.config.halt_max_steps
+
+            # Combine halt signals: Q or confidence or last-step
+            halted = is_last_step | (q_halt_logits > q_continue_logits) | (conf.squeeze(-1) >= self.config.glps_tau_halt)
+
+            # Exploration during training only
+            if self.training and (self.config.halt_max_steps > 1):
+                min_halt_steps = (torch.rand_like(q_halt_logits) < self.config.halt_exploration_prob) * torch.randint_like(new_steps, low=2, high=self.config.halt_max_steps + 1)
+                halted = halted & (new_steps >= min_halt_steps)
+                
+                # Optional target for Q-learning (kept similar to HRM)
+                next_conf = self.inner(new_inner_carry, new_current_data)[-1]
+                outputs["target_q_continue"] = torch.sigmoid(torch.where(is_last_step, q_halt_logits, torch.maximum(q_halt_logits, q_continue_logits)))
+            else:
+                # During eval, always use max_steps to ensure consistent reasoning depth (same as TRM/HRM eval behavior)
+                halted = is_last_step
+
+        return GLPS_ACTV1Carry(new_inner_carry, new_steps, halted, new_current_data), outputs
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_evalboost/losses.py b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_evalboost/losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..cdfb77718bc16f98cdd7da06eb5795c36b866aa7
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_evalboost/losses.py
@@ -0,0 +1,102 @@
+from typing import Any, Tuple, Dict, Sequence, Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+import math
+
+IGNORE_LABEL_ID = -100
+
+
+def s(x, epsilon=1e-30):
+    return torch.where(
+        x<0,
+        1/(1-x+ epsilon),
+        x + 1
+    )
+
+
+def log_stablemax(x, dim=-1):
+    s_x = s(x)
+    return torch.log(s_x/torch.sum(s_x, dim=dim, keepdim=True))
+
+
+def stablemax_cross_entropy(logits, labels, ignore_index: int = -100, valid_mask=None):
+    logprobs = log_stablemax(logits.to(torch.float64), dim=-1)
+
+    if valid_mask is None:
+        valid_mask = (labels != ignore_index)
+    transformed_labels = torch.where(valid_mask, labels, 0)
+    prediction_logprobs = torch.gather(logprobs, index=transformed_labels.to(torch.long).unsqueeze(-1), dim=-1).squeeze(-1)
+
+    return -torch.where(valid_mask, prediction_logprobs, 0)
+
+
+def softmax_cross_entropy(logits, labels, ignore_index: int = -100):
+    # Cast logits to f32
+    # Flatten logits
+    return F.cross_entropy(logits.to(torch.float32).view(-1, logits.shape[-1]), labels.to(torch.long).view(-1), ignore_index=ignore_index, reduction="none").view(labels.shape)
+
+
+class ACTLossHead(nn.Module):
+    def __init__(self, model: nn.Module, loss_type: str):
+        super().__init__()
+        self.model = model
+        self.loss_fn = globals()[loss_type]
+        
+    def initial_carry(self, *args, **kwargs):
+        return self.model.initial_carry(*args, **kwargs)  # type: ignore
+
+    def forward(
+        self,
+        return_keys: Sequence[str],
+        # Model args
+        **model_kwargs,
+    ) -> Tuple[Any, torch.Tensor, Dict[str, torch.Tensor], Optional[Dict[str, torch.Tensor]], torch.Tensor]:
+        # Model logits
+        # B x SeqLen x D
+        new_carry, outputs = self.model(**model_kwargs)
+        labels = new_carry.current_data["labels"]
+
+        with torch.no_grad():
+            # Preds
+            outputs["preds"] = torch.argmax(outputs["logits"], dim=-1)
+
+            # Correctness
+            mask = (labels != IGNORE_LABEL_ID)
+            loss_counts = mask.sum(-1)
+            loss_divisor = loss_counts.clamp_min(1).unsqueeze(-1)  # Avoid NaNs in division
+
+            is_correct = mask & (torch.argmax(outputs["logits"], dim=-1) == labels)
+            seq_is_correct = is_correct.sum(-1) == loss_counts
+            
+            # Metrics (halted)
+            valid_metrics = new_carry.halted & (loss_counts > 0)
+            metrics = {
+                "count": valid_metrics.sum(),
+                
+                "accuracy":       torch.where(valid_metrics, (is_correct.to(torch.float32) / loss_divisor).sum(-1), 0).sum(),
+                "exact_accuracy": (valid_metrics & seq_is_correct).sum(),
+
+                "q_halt_accuracy": (valid_metrics & ((outputs["q_halt_logits"] >= 0) == seq_is_correct)).sum(),
+                "steps":          torch.where(valid_metrics, new_carry.steps, 0).sum(),
+            }
+
+        # Losses
+
+        lm_loss = (self.loss_fn(outputs["logits"], labels, ignore_index=IGNORE_LABEL_ID, valid_mask=mask) / loss_divisor).sum()
+        q_halt_loss = F.binary_cross_entropy_with_logits(outputs["q_halt_logits"], seq_is_correct.to(outputs["q_halt_logits"].dtype), reduction="sum")
+        metrics.update({
+            "lm_loss": lm_loss.detach(),
+            "q_halt_loss": q_halt_loss.detach(),
+        })
+        # Q continue (bootstrapping target loss); Alexia: This fits Q-learning, but seems totally unecessary
+        q_continue_loss = 0
+        if "target_q_continue" in outputs:
+            q_continue_loss = F.binary_cross_entropy_with_logits(outputs["q_continue_logits"], outputs["target_q_continue"], reduction="sum")
+
+            metrics["q_continue_loss"] = q_continue_loss.detach()
+        # Filter outputs for return
+        detached_outputs = {k: outputs[k].detach() for k in return_keys if k in outputs}
+
+        return new_carry, lm_loss + 0.5 * (q_halt_loss + q_continue_loss), metrics, detached_outputs, new_carry.halted.all()
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_evalboost2/all_config.yaml b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_evalboost2/all_config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..c179d164ff52e9cb6a0aeee7012b2acaa367fffd
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_evalboost2/all_config.yaml
@@ -0,0 +1,60 @@
+arch:
+  H_cycles: 3
+  H_layers: 2
+  L_cycles: 1
+  L_layers: 7
+  dep_rank: 64
+  dep_topk: 12
+  expansion: 4.0
+  forward_dtype: bfloat16
+  glps_dep_graph: true
+  glps_enabled: true
+  glps_fill_obvious: true
+  glps_global_propagate_on_low_conf: true
+  glps_max_targeted_iters: 4
+  glps_tau_halt: 0.95
+  glps_tau_uncertain: 0.8
+  glps_token_masking: true
+  halt_exploration_prob: 0.1
+  halt_max_steps: 16
+  hidden_size: 512
+  loss:
+    loss_type: stablemax_cross_entropy
+    name: losses@ACTLossHead
+  mlp_t: false
+  name: recursive_reasoning.glps@GLPS_ACTV1
+  num_heads: 8
+  pos_encodings: rope
+  puzzle_emb_ndim: 0
+  rms_norm_eps: 1.0e-05
+  rope_theta: 10000.0
+beta1: 0.9
+beta2: 0.95
+checkpoint_every_eval: true
+checkpoint_path: checkpoints/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_evalboost2
+data_paths:
+- data/sudoku-extreme-1k-aug-1000
+data_paths_test: []
+ema: true
+ema_rate: 0.999
+epochs: 50000
+eval_glps_max_targeted_iters: 8
+eval_glps_tau_halt: 0.8
+eval_halt_max_steps: 48
+eval_interval: 5000
+eval_only: true
+eval_save_outputs: []
+evaluators: []
+freeze_weights: false
+global_batch_size: 768
+load_checkpoint: /teamspace/studios/this_studio/TinyRecursiveModels/checkpoints/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_decay_ft10k/step_58590
+lr: 0.0001
+lr_min_ratio: 0.01
+lr_warmup_steps: 2000
+min_eval_interval: 0
+project_name: Sudoku-extreme-1k-aug-1000-ACT-torch
+puzzle_emb_lr: 0.0001
+puzzle_emb_weight_decay: 1.0
+run_name: pretrain_glps_sudoku_v4_evalboost2
+seed: 0
+weight_decay: 1.0
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_evalboost2/glps.py b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_evalboost2/glps.py
new file mode 100644
index 0000000000000000000000000000000000000000..96e6de932e79e174cf773779293a643842e8a268
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_evalboost2/glps.py
@@ -0,0 +1,409 @@
+
+from typing import Tuple, List, Dict
+from dataclasses import dataclass
+import math
+import torch
+import torch.nn.functional as F
+from torch import nn
+from pydantic import BaseModel
+
+# Reuse the same building blocks as HRM/TRM
+from models.common import trunc_normal_init_
+from models.layers import rms_norm, SwiGLU, Attention, RotaryEmbedding, CosSin, CastedEmbedding, CastedLinear
+from models.sparse_embedding import CastedSparseEmbedding
+
+"""
+Global-Local Predictive Solver (GLPS)
+------------------------------------
+A light-weight control-policy on top of the HRM/TRM style blocks:
+- H1: global scan -> certainty map
+- L1: fill-obvious (lock stable cells)
+- H2: dependency scoring over remaining cells
+- L2: targeted refinement (masked updates)
+- H3: energy-based confidence -> (optional) one global propagate sweep -> halt
+
+This file keeps parameter growth tiny: a few heads + (optional) low-rank dependency scorer.
+"""
+
+@dataclass
+class GLPS_ACTV1InnerCarry:
+    z_H: torch.Tensor
+    z_L: torch.Tensor
+
+@dataclass
+class GLPS_ACTV1Carry:
+    inner_carry: GLPS_ACTV1InnerCarry
+    steps: torch.Tensor
+    halted: torch.Tensor
+    current_data: Dict[str, torch.Tensor]
+
+class GLPS_ACTV1Config(BaseModel):
+    # Core IO / shapes
+    batch_size: int
+    seq_len: int
+    puzzle_emb_ndim: int = 0
+    num_puzzle_identifiers: int = 1
+    vocab_size: int = 256
+
+    # Cycle schedule
+    H_cycles: int = 3              # (scan -> refine -> check) typical
+    L_cycles: int = 1
+
+    # Depth
+    H_layers: int = 2
+    L_layers: int = 4
+
+    # Transformer config
+    hidden_size: int = 512
+    expansion: float = 2.0
+    num_heads: int = 8
+    pos_encodings: str = "rope"
+
+    rms_norm_eps: float = 1e-5
+    rope_theta: float = 10000.0
+
+    # ACT wrapper
+    halt_max_steps: int = 4
+    halt_exploration_prob: float = 0.1
+
+    forward_dtype: str = "bfloat16"
+
+    # Optional: use MLP on L instead of attention (matches HRM/TRM option)
+    mlp_t: bool = False
+
+    # ---- GLPS extras (tiny) ----
+    glps_enabled: bool = True
+    glps_fill_obvious: bool = True
+    glps_dep_graph: bool = True
+    glps_token_masking: bool = True
+    glps_global_propagate_on_low_conf: bool = True
+
+    glps_tau_halt: float = 0.95       # final confidence to halt
+    glps_tau_uncertain: float = 0.60  # cell-wise certainty threshold
+    glps_max_targeted_iters: int = 2  # small number: 1-2
+
+    # Dependency scorer (low rank bilinear)
+    dep_rank: int = 32
+    dep_topk: int = 8
+
+class GLPSBlock(nn.Module):
+    def __init__(self, config: GLPS_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        if self.config.mlp_t:
+            self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size)
+            self.mlp_t = SwiGLU(
+                hidden_size=self.config.seq_len + self.puzzle_emb_len, # treat sequence as channel
+                expansion=config.expansion,
+            )
+        else:
+            self.self_attn = Attention(
+                hidden_size=config.hidden_size,
+                head_dim=config.hidden_size // config.num_heads,
+                num_heads=config.num_heads,
+                num_key_value_heads=config.num_heads,
+                causal=False,
+            )
+        self.mlp = SwiGLU(hidden_size=config.hidden_size, expansion=config.expansion)
+        self.norm_eps = config.rms_norm_eps
+
+    def forward(self, cos_sin: CosSin, hidden_states: torch.Tensor) -> torch.Tensor:
+        if self.config.mlp_t:
+            # MLP over sequence dimension (mlp-t)
+            hidden_states = hidden_states.transpose(1, 2)
+            out = self.mlp_t(hidden_states)
+            hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+            hidden_states = hidden_states.transpose(1, 2)
+        else:
+            hidden_states = rms_norm(
+                hidden_states + self.self_attn(cos_sin=cos_sin, hidden_states=hidden_states),
+                variance_epsilon=self.norm_eps,
+            )
+        out = self.mlp(hidden_states)
+        hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+        return hidden_states
+
+class GLPSReasoningModule(nn.Module):
+    """Reasoning stack with optional masked updates (only update uncertain tokens)."""
+    def __init__(self, layers: List[GLPSBlock]):
+        super().__init__()
+        self.layers = torch.nn.ModuleList(layers)
+
+    def forward(self, hidden_states: torch.Tensor, input_injection: torch.Tensor, update_mask: torch.Tensor = None, **kwargs) -> torch.Tensor:
+        x = hidden_states
+        for layer in self.layers:
+            # Compute candidate update using injected context
+            y = layer(hidden_states=x + input_injection, **kwargs)
+            if update_mask is not None:
+                # Convex blend keeps frozen tokens unchanged
+                m = update_mask.to(x.dtype)[..., None]
+                x = x + m * (y - x)
+            else:
+                x = y
+        return x
+
+class GLPS_ACTV1_Inner(nn.Module):
+    def __init__(self, config: GLPS_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        self.forward_dtype = getattr(torch, self.config.forward_dtype)
+
+        # I/O
+        self.embed_scale  = math.sqrt(self.config.hidden_size)
+        embed_init_std = 1.0 / self.embed_scale
+
+        self.embed_tokens = CastedEmbedding(self.config.vocab_size, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+        self.lm_head      = CastedLinear(self.config.hidden_size, self.config.vocab_size, bias=False)
+        self.q_head       = CastedLinear(self.config.hidden_size, 2, bias=True)
+
+        # Puzzle emb (optional) — same convention as HRM/TRM
+        self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size)  # ceil div
+        if self.config.puzzle_emb_ndim > 0:
+            self.puzzle_emb = CastedSparseEmbedding(self.config.num_puzzle_identifiers, self.config.puzzle_emb_ndim, batch_size=self.config.batch_size, init_std=0, cast_to=self.forward_dtype)
+
+        # Positional encodings
+        if self.config.pos_encodings == "rope":
+            self.rotary_emb = RotaryEmbedding(dim=self.config.hidden_size // self.config.num_heads, max_position_embeddings=self.config.seq_len + self.puzzle_emb_len, base=self.config.rope_theta)
+        elif self.config.pos_encodings == "learned":
+            self.embed_pos = CastedEmbedding(self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+
+        # Reasoning stacks
+        self.H_level = GLPSReasoningModule(layers=[GLPSBlock(self.config) for _ in range(self.config.H_layers)])
+        self.L_level = GLPSReasoningModule(layers=[GLPSBlock(self.config) for _ in range(self.config.L_layers)])
+
+        # Initial states (match HRM/TRM style)
+        H_init = trunc_normal_init_(torch.empty(1, 1, self.config.hidden_size, dtype=self.forward_dtype), std=1.0)
+        L_init = trunc_normal_init_(torch.empty(1, 1, self.config.hidden_size, dtype=self.forward_dtype), std=1.0)
+        self.register_buffer("H_init", H_init, persistent=True)
+        self.register_buffer("L_init", L_init, persistent=True)
+
+        # GLPS small heads
+        self.candidate_head = CastedLinear(self.config.hidden_size, 16, bias=True)  # task-specific; for Sudoku you can slice to 9
+        self.certainty_head = CastedLinear(self.config.hidden_size, 1, bias=True)
+        self.energy_head    = CastedLinear(self.config.hidden_size, 1, bias=True)
+
+        # Low-rank dependency scorer (shared)
+        r = max(1, self.config.dep_rank)
+        self.dep_q = CastedLinear(self.config.hidden_size, r, bias=False)
+        self.dep_k = CastedLinear(self.config.hidden_size, r, bias=False)
+
+        # Q head init like HRM/TRM (near-zero -> easier bootstrapping)
+        with torch.no_grad():
+            self.q_head.weight.zero_()
+            self.q_head.bias.fill_(-5)
+
+    def _input_embeddings(self, input: torch.Tensor, puzzle_identifiers: torch.Tensor):
+        # Token embedding
+        embedding = self.embed_tokens(input.to(torch.int32))
+
+        # Puzzle embeddings
+        if self.config.puzzle_emb_ndim > 0:
+            puzzle_embedding = self.puzzle_emb(puzzle_identifiers)
+            pad_count = self.puzzle_emb_len * self.config.hidden_size - puzzle_embedding.shape[-1]
+            if pad_count > 0:
+                puzzle_embedding = F.pad(puzzle_embedding, (0, pad_count))
+            embedding = torch.cat((puzzle_embedding.view(-1, self.puzzle_emb_len, self.config.hidden_size), embedding), dim=-2)
+
+        # Position embeddings
+        if self.config.pos_encodings == "learned":
+            embedding = 0.707106781 * (embedding + self.embed_pos.embedding_weight.to(self.forward_dtype))
+
+        return self.embed_scale * embedding
+
+    def empty_carry(self, batch_size: int):
+        return GLPS_ACTV1InnerCarry(
+            z_H=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+            z_L=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+        )
+
+    def reset_carry(self, reset_flag: torch.Tensor, carry: GLPS_ACTV1InnerCarry):
+        # Explicitly expand buffers and mask to target shapes to avoid shape confusion
+        B, L, D = carry.z_H.shape
+        # Reduce/reset flag to per-batch boolean vector of shape [B]
+        if reset_flag.ndim == 1 and reset_flag.shape[0] == B:
+            reset_b = reset_flag.to(torch.bool)
+        else:
+            # If shape is [B, ...] reduce across non-batch dims; otherwise fallback to first B entries
+            try:
+                reset_b = reset_flag.reshape(B, -1).any(dim=1).to(torch.bool)
+            except Exception:
+                reset_b = reset_flag.reshape(-1)[:B].to(torch.bool)
+        m = reset_b.view(B, 1, 1)
+        mH = m.expand(B, L, D)
+        mL = mH  # same shape for z_L
+        H_init_exp = self.H_init.expand(B, L, D)
+        L_init_exp = self.L_init.expand(B, L, D)
+        return GLPS_ACTV1InnerCarry(
+            z_H=torch.where(mH, H_init_exp, carry.z_H),
+            z_L=torch.where(mL, L_init_exp, carry.z_L),
+        )
+
+    def _global_scan(self, z_L, z_H, input_embeddings, seq_info):
+        # One light pass to gather global signals
+        z_scan = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+        cand_logits = self.candidate_head(z_scan)              # [B, L, C]
+        certainty   = torch.sigmoid(self.certainty_head(z_scan))  # [B, L, 1]
+        return z_scan, cand_logits, certainty
+
+    def _build_dep_mask(self, z_ctx, uncertain_mask: torch.Tensor):
+        """Compute a dependency-based focus mask from a low-rank bilinear score.
+        uncertain_mask: [B, L] boolean mask of cells that are currently uncertain
+        Returns: dep_mask [B, L] boolean mask of cells to (re)update.
+        """
+        B, L, D = z_ctx.shape
+        # Project to low-rank space; use float32 to avoid dtype mismatches under torch.compile
+        Q = self.dep_q(z_ctx).to(torch.float32)   # [B, L, r]
+        K = self.dep_k(z_ctx).to(torch.float32)   # [B, L, r]
+        r = max(1, int(Q.shape[-1]))
+        sim = torch.matmul(Q, K.transpose(1, 2))  # [B, L, L] (float32)
+        sim = sim / math.sqrt(r)
+
+        # Aggregate influence from uncertain queries onto target tokens
+        src = uncertain_mask.to(sim.dtype).unsqueeze(1)      # [B, 1, L]
+        influence = torch.matmul(src, sim).squeeze(1)        # [B, L]
+
+        # Top-k influenced tokens per batch
+        topk = min(self.config.dep_topk, L)
+        vals, idx = torch.topk(influence, k=topk, dim=-1)    # [B, topk]
+        dep_mask = torch.zeros_like(uncertain_mask)
+        dep_mask.scatter_(1, idx, True)
+
+        # Always include uncertain cells themselves
+        dep_mask = dep_mask | uncertain_mask
+        return dep_mask
+
+    def forward(self, carry: GLPS_ACTV1InnerCarry, batch: Dict[str, torch.Tensor]):
+        seq_info = dict(cos_sin=getattr(self, "rotary_emb", None)() if hasattr(self, "rotary_emb") else None)
+
+        # Encode inputs
+        input_embeddings = self._input_embeddings(batch["inputs"], batch["puzzle_identifiers"])
+
+        # States
+        z_H, z_L = carry.z_H, carry.z_L
+
+        if not self.config.glps_enabled:
+            # Fallback to an HRM-like single-cycle grad update for compatibility
+            with torch.no_grad():
+                for _H in range(self.config.H_cycles - 1):
+                    for _L in range(self.config.L_cycles):
+                        z_L = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+                    z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+            # final grad step
+            for _L in range(self.config.L_cycles):
+                z_L = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+            z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+
+            # Outputs
+            new_carry = GLPS_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach())
+            logits = self.lm_head(z_H)[:, self.puzzle_emb_len:]
+            q_logits = self.q_head(z_H[:, 0]).to(torch.float32)
+            conf = torch.zeros_like(q_logits[..., :1]) + 0.5  # neutral
+            return new_carry, logits, (q_logits[..., 0], q_logits[..., 1]), conf
+
+        # ===== GLPS path =====
+        # H1: global scan (cheap)
+        with torch.no_grad():
+            z_scan, cand_logits, certainty = self._global_scan(z_L, z_H, input_embeddings, seq_info)
+
+        # L1: fill-obvious -> compute stable vs uncertain masks
+        if self.config.glps_fill_obvious:
+            obvious_mask = (certainty >= self.config.glps_tau_uncertain)  # [B, L, 1]
+        else:
+            obvious_mask = torch.zeros_like(certainty).bool()
+        stable_mask = obvious_mask.squeeze(-1)         # [B, L]
+        uncertain_mask = ~stable_mask                  # [B, L]
+
+        # H2: dependency prediction over remaining cells
+        if self.config.glps_dep_graph:
+            dep_mask = self._build_dep_mask(z_scan, uncertain_mask)   # [B, L]
+        else:
+            dep_mask = uncertain_mask
+
+        # L2: targeted refinement (a couple of masked iters)
+        update_mask = dep_mask if self.config.glps_token_masking else None
+        z = z_scan.detach()  # use scanned context as start
+        for _ in range(self.config.glps_max_targeted_iters):
+            z = self.L_level(z, z_H + input_embeddings, update_mask=update_mask, **seq_info)
+            # Refresh certainty to shrink mask (optional but cheap)
+            cert_now = torch.sigmoid(self.certainty_head(z)).squeeze(-1)
+            if self.config.glps_token_masking:
+                update_mask = dep_mask & (cert_now < self.config.glps_tau_uncertain)
+
+        # Merge into H and do a light H update with grad
+        z_L = z
+        z_H = self.H_level(z_H, z_L, update_mask=None, **seq_info)
+
+        # H3: energy/consistency -> confidence & optional global propagate
+        energy = torch.sigmoid(self.energy_head(z_H)).mean(dim=1)  # [B, 1]
+        conf = 1.0 - energy
+        need_sweep = (conf.squeeze(-1) < self.config.glps_tau_halt)
+        if self.config.glps_global_propagate_on_low_conf and need_sweep.any():
+            # one final full sweep only for rows needing it
+            maskB = need_sweep.view(-1, 1, 1)
+            zL2 = self.L_level(z_L, z_H + input_embeddings, update_mask=None, **seq_info)
+            zH2 = self.H_level(z_H, zL2, update_mask=None, **seq_info)
+            z_L = torch.where(maskB, zL2, z_L)
+            z_H = torch.where(maskB, zH2, z_H)
+
+        # Outputs
+        new_carry = GLPS_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach())
+        logits = self.lm_head(z_H)[:, self.puzzle_emb_len:]
+        q_logits = self.q_head(z_H[:, 0]).to(torch.float32)
+        return new_carry, logits, (q_logits[..., 0], q_logits[..., 1]), conf
+
+class GLPS_ACTV1(nn.Module):
+    """ACT-style wrapper that mixes Q-halt with GLPS confidence."""
+    def __init__(self, config_dict: dict):
+        super().__init__()
+        self.config = GLPS_ACTV1Config(**config_dict)
+        self.inner = GLPS_ACTV1_Inner(self.config)
+
+    @property
+    def puzzle_emb(self):
+        return self.inner.puzzle_emb
+
+    def initial_carry(self, batch: Dict[str, torch.Tensor]):
+        batch_size = batch["inputs"].shape[0]
+        return GLPS_ACTV1Carry(
+            inner_carry=self.inner.empty_carry(batch_size),
+            steps=torch.zeros((batch_size,), dtype=torch.int32),
+            halted=torch.ones((batch_size,), dtype=torch.bool),  # start halted to force reset on first pass
+            current_data={k: torch.empty_like(v) for k, v in batch.items()}
+        )
+
+    def forward(self, carry: GLPS_ACTV1Carry, batch: Dict[str, torch.Tensor]):
+        # Reset halted seqs
+        new_inner_carry = self.inner.reset_carry(carry.halted, carry.inner_carry)
+        new_steps = torch.where(carry.halted, 0, carry.steps)
+        new_current_data = {k: torch.where(carry.halted.view((-1,) + (1,) * (batch[k].ndim - 1)), batch[k], v) for k, v in carry.current_data.items()}
+
+        # Inner step
+        new_inner_carry, logits, (q_halt_logits, q_continue_logits), conf = self.inner(new_inner_carry, new_current_data)
+
+        outputs = {
+            "logits": logits,
+            "q_halt_logits": q_halt_logits,
+            "q_continue_logits": q_continue_logits,
+            "conf": conf.squeeze(-1),
+        }
+
+        with torch.no_grad():
+            new_steps = new_steps + 1
+            is_last_step = new_steps >= self.config.halt_max_steps
+
+            # Combine halt signals: Q or confidence or last-step
+            halted = is_last_step | (q_halt_logits > q_continue_logits) | (conf.squeeze(-1) >= self.config.glps_tau_halt)
+
+            # Exploration during training only
+            if self.training and (self.config.halt_max_steps > 1):
+                min_halt_steps = (torch.rand_like(q_halt_logits) < self.config.halt_exploration_prob) * torch.randint_like(new_steps, low=2, high=self.config.halt_max_steps + 1)
+                halted = halted & (new_steps >= min_halt_steps)
+                
+                # Optional target for Q-learning (kept similar to HRM)
+                next_conf = self.inner(new_inner_carry, new_current_data)[-1]
+                outputs["target_q_continue"] = torch.sigmoid(torch.where(is_last_step, q_halt_logits, torch.maximum(q_halt_logits, q_continue_logits)))
+            else:
+                # During eval, always use max_steps to ensure consistent reasoning depth (same as TRM/HRM eval behavior)
+                halted = is_last_step
+
+        return GLPS_ACTV1Carry(new_inner_carry, new_steps, halted, new_current_data), outputs
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_evalboost2/losses.py b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_evalboost2/losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..cdfb77718bc16f98cdd7da06eb5795c36b866aa7
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_glps_sudoku_v4_evalboost2/losses.py
@@ -0,0 +1,102 @@
+from typing import Any, Tuple, Dict, Sequence, Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+import math
+
+IGNORE_LABEL_ID = -100
+
+
+def s(x, epsilon=1e-30):
+    return torch.where(
+        x<0,
+        1/(1-x+ epsilon),
+        x + 1
+    )
+
+
+def log_stablemax(x, dim=-1):
+    s_x = s(x)
+    return torch.log(s_x/torch.sum(s_x, dim=dim, keepdim=True))
+
+
+def stablemax_cross_entropy(logits, labels, ignore_index: int = -100, valid_mask=None):
+    logprobs = log_stablemax(logits.to(torch.float64), dim=-1)
+
+    if valid_mask is None:
+        valid_mask = (labels != ignore_index)
+    transformed_labels = torch.where(valid_mask, labels, 0)
+    prediction_logprobs = torch.gather(logprobs, index=transformed_labels.to(torch.long).unsqueeze(-1), dim=-1).squeeze(-1)
+
+    return -torch.where(valid_mask, prediction_logprobs, 0)
+
+
+def softmax_cross_entropy(logits, labels, ignore_index: int = -100):
+    # Cast logits to f32
+    # Flatten logits
+    return F.cross_entropy(logits.to(torch.float32).view(-1, logits.shape[-1]), labels.to(torch.long).view(-1), ignore_index=ignore_index, reduction="none").view(labels.shape)
+
+
+class ACTLossHead(nn.Module):
+    def __init__(self, model: nn.Module, loss_type: str):
+        super().__init__()
+        self.model = model
+        self.loss_fn = globals()[loss_type]
+        
+    def initial_carry(self, *args, **kwargs):
+        return self.model.initial_carry(*args, **kwargs)  # type: ignore
+
+    def forward(
+        self,
+        return_keys: Sequence[str],
+        # Model args
+        **model_kwargs,
+    ) -> Tuple[Any, torch.Tensor, Dict[str, torch.Tensor], Optional[Dict[str, torch.Tensor]], torch.Tensor]:
+        # Model logits
+        # B x SeqLen x D
+        new_carry, outputs = self.model(**model_kwargs)
+        labels = new_carry.current_data["labels"]
+
+        with torch.no_grad():
+            # Preds
+            outputs["preds"] = torch.argmax(outputs["logits"], dim=-1)
+
+            # Correctness
+            mask = (labels != IGNORE_LABEL_ID)
+            loss_counts = mask.sum(-1)
+            loss_divisor = loss_counts.clamp_min(1).unsqueeze(-1)  # Avoid NaNs in division
+
+            is_correct = mask & (torch.argmax(outputs["logits"], dim=-1) == labels)
+            seq_is_correct = is_correct.sum(-1) == loss_counts
+            
+            # Metrics (halted)
+            valid_metrics = new_carry.halted & (loss_counts > 0)
+            metrics = {
+                "count": valid_metrics.sum(),
+                
+                "accuracy":       torch.where(valid_metrics, (is_correct.to(torch.float32) / loss_divisor).sum(-1), 0).sum(),
+                "exact_accuracy": (valid_metrics & seq_is_correct).sum(),
+
+                "q_halt_accuracy": (valid_metrics & ((outputs["q_halt_logits"] >= 0) == seq_is_correct)).sum(),
+                "steps":          torch.where(valid_metrics, new_carry.steps, 0).sum(),
+            }
+
+        # Losses
+
+        lm_loss = (self.loss_fn(outputs["logits"], labels, ignore_index=IGNORE_LABEL_ID, valid_mask=mask) / loss_divisor).sum()
+        q_halt_loss = F.binary_cross_entropy_with_logits(outputs["q_halt_logits"], seq_is_correct.to(outputs["q_halt_logits"].dtype), reduction="sum")
+        metrics.update({
+            "lm_loss": lm_loss.detach(),
+            "q_halt_loss": q_halt_loss.detach(),
+        })
+        # Q continue (bootstrapping target loss); Alexia: This fits Q-learning, but seems totally unecessary
+        q_continue_loss = 0
+        if "target_q_continue" in outputs:
+            q_continue_loss = F.binary_cross_entropy_with_logits(outputs["q_continue_logits"], outputs["target_q_continue"], reduction="sum")
+
+            metrics["q_continue_loss"] = q_continue_loss.detach()
+        # Filter outputs for return
+        detached_outputs = {k: outputs[k].detach() for k in return_keys if k in outputs}
+
+        return new_carry, lm_loss + 0.5 * (q_halt_loss + q_continue_loss), metrics, detached_outputs, new_carry.halted.all()
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_hybrid_sudoku/all_config.yaml b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_hybrid_sudoku/all_config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..640ff80d97fea13e662387d0b4591aff06eb5b77
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_hybrid_sudoku/all_config.yaml
@@ -0,0 +1,778 @@
+arch:
+  H_cycles: 3
+  H_layers: 0
+  L_cycles: 6
+  L_layers: 2
+  expansion: 4
+  ffn_hybrid: true
+  ffn_hybrid_gate: scalar
+  ffn_kron_chunk_k: 16
+  ffn_kron_chunk_t: 4096
+  ffn_kron_in_factors: &id001 !!python/object:omegaconf.listconfig.ListConfig
+    _content:
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+      _metadata: !!python/object:omegaconf.base.Metadata
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+        resolver_cache: !!python/object/apply:collections.defaultdict
+        - &id003 !!python/name:builtins.dict ''
+      _parent: *id001
+      _val: 32
+    - !!python/object:omegaconf.nodes.AnyNode
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+      object_type: &id007 !!python/name:builtins.list ''
+      optional: true
+      ref_type: *id002
+      resolver_cache: !!python/object/apply:collections.defaultdict
+      - *id003
+    _parent: &id004 !!python/object:omegaconf.dictconfig.DictConfig
+      _content:
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+          _metadata: !!python/object:omegaconf.base.Metadata
+            flags: {}
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+          _val: 3
+        H_layers: !!python/object:omegaconf.nodes.AnyNode
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+          _val: 0
+        L_cycles: !!python/object:omegaconf.nodes.AnyNode
+          _metadata: !!python/object:omegaconf.base.Metadata
+            flags: {}
+            flags_root: false
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+            - *id003
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+          _val: 6
+        L_layers: !!python/object:omegaconf.nodes.AnyNode
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+            flags: {}
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+            - *id003
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+          _val: 2
+        expansion: !!python/object:omegaconf.nodes.AnyNode
+          _metadata: !!python/object:omegaconf.base.Metadata
+            flags: {}
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+            key: expansion
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+            ref_type: *id002
+            resolver_cache: !!python/object/apply:collections.defaultdict
+            - *id003
+          _parent: *id004
+          _val: 4
+        ffn_hybrid: !!python/object:omegaconf.nodes.AnyNode
+          _metadata: !!python/object:omegaconf.base.Metadata
+            flags: {}
+            flags_root: false
+            key: ffn_hybrid
+            object_type: null
+            optional: true
+            ref_type: *id002
+            resolver_cache: !!python/object/apply:collections.defaultdict
+            - *id003
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+          _val: true
+        ffn_hybrid_gate: !!python/object:omegaconf.nodes.AnyNode
+          _metadata: !!python/object:omegaconf.base.Metadata
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+            resolver_cache: !!python/object/apply:collections.defaultdict
+            - *id003
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+          _val: scalar
+        ffn_kron_chunk_k: !!python/object:omegaconf.nodes.AnyNode
+          _metadata: !!python/object:omegaconf.base.Metadata
+            flags: {}
+            flags_root: false
+            key: ffn_kron_chunk_k
+            object_type: null
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+            - *id003
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+        ffn_kron_chunk_t: !!python/object:omegaconf.nodes.AnyNode
+          _metadata: !!python/object:omegaconf.base.Metadata
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+            key: ffn_kron_chunk_t
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+            resolver_cache: !!python/object/apply:collections.defaultdict
+            - *id003
+          _parent: *id004
+          _val: 4096
+        ffn_kron_in_factors: *id001
+        ffn_kron_out_factors: &id005 !!python/object:omegaconf.listconfig.ListConfig
+          _content:
+          - !!python/object:omegaconf.nodes.AnyNode
+            _metadata: !!python/object:omegaconf.base.Metadata
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+              key: 0
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+            - *id003
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+          _val: true
+        ffn_kron_terms: !!python/object:omegaconf.nodes.AnyNode
+          _metadata: !!python/object:omegaconf.base.Metadata
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+            flags_root: false
+            key: ffn_kron_terms
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+          _val: 96
+        ffn_rank: !!python/object:omegaconf.nodes.AnyNode
+          _metadata: !!python/object:omegaconf.base.Metadata
+            flags: {}
+            flags_root: false
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+          _metadata: !!python/object:omegaconf.base.Metadata
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+            key: ffn_type
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+            optional: true
+            ref_type: *id002
+            resolver_cache: !!python/object/apply:collections.defaultdict
+            - *id003
+          _parent: *id004
+          _val: mlp
+        forward_dtype: !!python/object:omegaconf.nodes.AnyNode
+          _metadata: !!python/object:omegaconf.base.Metadata
+            flags: {}
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+        halt_exploration_prob: !!python/object:omegaconf.nodes.AnyNode
+          _metadata: !!python/object:omegaconf.base.Metadata
+            flags: {}
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+        loss: &id008 !!python/object:omegaconf.dictconfig.DictConfig
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+              _val: stablemax_cross_entropy
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+              _val: losses@ACTLossHead
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+        mlp_t: !!python/object:omegaconf.nodes.AnyNode
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+          _val: true
+        name: !!python/object:omegaconf.nodes.AnyNode
+          _metadata: !!python/object:omegaconf.base.Metadata
+            flags: {}
+            flags_root: false
+            key: name
+            object_type: null
+            optional: true
+            ref_type: *id002
+            resolver_cache: !!python/object/apply:collections.defaultdict
+            - *id003
+          _parent: *id004
+          _val: recursive_reasoning.trm@TinyRecursiveReasoningModel_ACTV1
+        no_ACT_continue: !!python/object:omegaconf.nodes.AnyNode
+          _metadata: !!python/object:omegaconf.base.Metadata
+            flags: {}
+            flags_root: false
+            key: no_ACT_continue
+            object_type: null
+            optional: true
+            ref_type: *id002
+            resolver_cache: !!python/object/apply:collections.defaultdict
+            - *id003
+          _parent: *id004
+          _val: true
+        num_heads: !!python/object:omegaconf.nodes.AnyNode
+          _metadata: !!python/object:omegaconf.base.Metadata
+            flags: {}
+            flags_root: false
+            key: num_heads
+            object_type: null
+            optional: true
+            ref_type: *id002
+            resolver_cache: !!python/object/apply:collections.defaultdict
+            - *id003
+          _parent: *id004
+          _val: 8
+        pos_encodings: !!python/object:omegaconf.nodes.AnyNode
+          _metadata: !!python/object:omegaconf.base.Metadata
+            flags: {}
+            flags_root: false
+            key: pos_encodings
+            object_type: null
+            optional: true
+            ref_type: *id002
+            resolver_cache: !!python/object/apply:collections.defaultdict
+            - *id003
+          _parent: *id004
+          _val: rope
+        puzzle_emb_len: !!python/object:omegaconf.nodes.AnyNode
+          _metadata: !!python/object:omegaconf.base.Metadata
+            flags: {}
+            flags_root: false
+            key: puzzle_emb_len
+            object_type: null
+            optional: true
+            ref_type: *id002
+            resolver_cache: !!python/object/apply:collections.defaultdict
+            - *id003
+          _parent: *id004
+          _val: 16
+        puzzle_emb_ndim: !!python/object:omegaconf.nodes.AnyNode
+          _metadata: !!python/object:omegaconf.base.Metadata
+            flags: {}
+            flags_root: false
+            key: puzzle_emb_ndim
+            object_type: null
+            optional: true
+            ref_type: *id002
+            resolver_cache: !!python/object/apply:collections.defaultdict
+            - *id003
+          _parent: *id004
+          _val: ${.hidden_size}
+      _metadata: !!python/object:omegaconf.base.ContainerMetadata
+        element_type: *id002
+        flags: {}
+        flags_root: false
+        key: arch
+        key_type: *id002
+        object_type: *id003
+        optional: true
+        ref_type: *id002
+        resolver_cache: !!python/object/apply:collections.defaultdict
+        - *id003
+      _parent: &id009 !!python/object:omegaconf.dictconfig.DictConfig
+        _content:
+          arch: *id004
+          beta1: !!python/object:omegaconf.nodes.AnyNode
+            _metadata: !!python/object:omegaconf.base.Metadata
+              flags: {}
+              flags_root: false
+              key: beta1
+              object_type: null
+              optional: true
+              ref_type: *id002
+              resolver_cache: !!python/object/apply:collections.defaultdict
+              - *id003
+            _parent: *id009
+            _val: 0.9
+          beta2: !!python/object:omegaconf.nodes.AnyNode
+            _metadata: !!python/object:omegaconf.base.Metadata
+              flags: {}
+              flags_root: false
+              key: beta2
+              object_type: null
+              optional: true
+              ref_type: *id002
+              resolver_cache: !!python/object/apply:collections.defaultdict
+              - *id003
+            _parent: *id009
+            _val: 0.95
+          checkpoint_every_eval: !!python/object:omegaconf.nodes.AnyNode
+            _metadata: !!python/object:omegaconf.base.Metadata
+              flags: {}
+              flags_root: false
+              key: checkpoint_every_eval
+              object_type: null
+              optional: true
+              ref_type: *id002
+              resolver_cache: !!python/object/apply:collections.defaultdict
+              - *id003
+            _parent: *id009
+            _val: true
+          data_paths: &id010 !!python/object:omegaconf.listconfig.ListConfig
+            _content:
+            - !!python/object:omegaconf.nodes.AnyNode
+              _metadata: !!python/object:omegaconf.base.Metadata
+                flags: {}
+                flags_root: false
+                key: 0
+                object_type: null
+                optional: true
+                ref_type: *id002
+                resolver_cache: !!python/object/apply:collections.defaultdict
+                - *id003
+              _parent: *id010
+              _val: data/sudoku-extreme-1k-aug-1000
+            _metadata: !!python/object:omegaconf.base.ContainerMetadata
+              element_type: *id002
+              flags: {}
+              flags_root: false
+              key: data_paths
+              key_type: *id006
+              object_type: *id007
+              optional: true
+              ref_type: *id002
+              resolver_cache: !!python/object/apply:collections.defaultdict
+              - *id003
+            _parent: *id009
+          data_paths_test: !!python/object:omegaconf.listconfig.ListConfig
+            _content: []
+            _metadata: !!python/object:omegaconf.base.ContainerMetadata
+              element_type: *id002
+              flags: {}
+              flags_root: false
+              key: data_paths_test
+              key_type: *id006
+              object_type: *id007
+              optional: true
+              ref_type: *id002
+              resolver_cache: !!python/object/apply:collections.defaultdict
+              - *id003
+            _parent: *id009
+          ema: !!python/object:omegaconf.nodes.AnyNode
+            _metadata: !!python/object:omegaconf.base.Metadata
+              flags: {}
+              flags_root: false
+              key: ema
+              object_type: null
+              optional: true
+              ref_type: *id002
+              resolver_cache: !!python/object/apply:collections.defaultdict
+              - *id003
+            _parent: *id009
+            _val: true
+          ema_rate: !!python/object:omegaconf.nodes.AnyNode
+            _metadata: !!python/object:omegaconf.base.Metadata
+              flags: {}
+              flags_root: false
+              key: ema_rate
+              object_type: null
+              optional: true
+              ref_type: *id002
+              resolver_cache: !!python/object/apply:collections.defaultdict
+              - *id003
+            _parent: *id009
+            _val: 0.999
+          epochs: !!python/object:omegaconf.nodes.AnyNode
+            _metadata: !!python/object:omegaconf.base.Metadata
+              flags: {}
+              flags_root: false
+              key: epochs
+              object_type: null
+              optional: true
+              ref_type: *id002
+              resolver_cache: !!python/object/apply:collections.defaultdict
+              - *id003
+            _parent: *id009
+            _val: 50000
+          eval_interval: !!python/object:omegaconf.nodes.AnyNode
+            _metadata: !!python/object:omegaconf.base.Metadata
+              flags: {}
+              flags_root: false
+              key: eval_interval
+              object_type: null
+              optional: true
+              ref_type: *id002
+              resolver_cache: !!python/object/apply:collections.defaultdict
+              - *id003
+            _parent: *id009
+            _val: 5000
+          evaluators: !!python/object:omegaconf.listconfig.ListConfig
+            _content: []
+            _metadata: !!python/object:omegaconf.base.ContainerMetadata
+              element_type: *id002
+              flags: {}
+              flags_root: false
+              key: evaluators
+              key_type: *id006
+              object_type: *id007
+              optional: true
+              ref_type: *id002
+              resolver_cache: !!python/object/apply:collections.defaultdict
+              - *id003
+            _parent: *id009
+          freeze_weights: !!python/object:omegaconf.nodes.AnyNode
+            _metadata: !!python/object:omegaconf.base.Metadata
+              flags: {}
+              flags_root: false
+              key: freeze_weights
+              object_type: null
+              optional: true
+              ref_type: *id002
+              resolver_cache: !!python/object/apply:collections.defaultdict
+              - *id003
+            _parent: *id009
+            _val: false
+          global_batch_size: !!python/object:omegaconf.nodes.AnyNode
+            _metadata: !!python/object:omegaconf.base.Metadata
+              flags: {}
+              flags_root: false
+              key: global_batch_size
+              object_type: null
+              optional: true
+              ref_type: *id002
+              resolver_cache: !!python/object/apply:collections.defaultdict
+              - *id003
+            _parent: *id009
+            _val: 768
+          lr: !!python/object:omegaconf.nodes.AnyNode
+            _metadata: !!python/object:omegaconf.base.Metadata
+              flags: {}
+              flags_root: false
+              key: lr
+              object_type: null
+              optional: true
+              ref_type: *id002
+              resolver_cache: !!python/object/apply:collections.defaultdict
+              - *id003
+            _parent: *id009
+            _val: 0.0001
+          lr_min_ratio: !!python/object:omegaconf.nodes.AnyNode
+            _metadata: !!python/object:omegaconf.base.Metadata
+              flags: {}
+              flags_root: false
+              key: lr_min_ratio
+              object_type: null
+              optional: true
+              ref_type: *id002
+              resolver_cache: !!python/object/apply:collections.defaultdict
+              - *id003
+            _parent: *id009
+            _val: 1.0
+          lr_warmup_steps: !!python/object:omegaconf.nodes.AnyNode
+            _metadata: !!python/object:omegaconf.base.Metadata
+              flags: {}
+              flags_root: false
+              key: lr_warmup_steps
+              object_type: null
+              optional: true
+              ref_type: *id002
+              resolver_cache: !!python/object/apply:collections.defaultdict
+              - *id003
+            _parent: *id009
+            _val: 2000
+          min_eval_interval: !!python/object:omegaconf.nodes.AnyNode
+            _metadata: !!python/object:omegaconf.base.Metadata
+              flags: {}
+              flags_root: false
+              key: min_eval_interval
+              object_type: null
+              optional: true
+              ref_type: *id002
+              resolver_cache: !!python/object/apply:collections.defaultdict
+              - *id003
+            _parent: *id009
+            _val: 0
+          puzzle_emb_lr: !!python/object:omegaconf.nodes.AnyNode
+            _metadata: !!python/object:omegaconf.base.Metadata
+              flags: {}
+              flags_root: false
+              key: puzzle_emb_lr
+              object_type: null
+              optional: true
+              ref_type: *id002
+              resolver_cache: !!python/object/apply:collections.defaultdict
+              - *id003
+            _parent: *id009
+            _val: 0.0001
+          puzzle_emb_weight_decay: !!python/object:omegaconf.nodes.AnyNode
+            _metadata: !!python/object:omegaconf.base.Metadata
+              flags: {}
+              flags_root: false
+              key: puzzle_emb_weight_decay
+              object_type: null
+              optional: true
+              ref_type: *id002
+              resolver_cache: !!python/object/apply:collections.defaultdict
+              - *id003
+            _parent: *id009
+            _val: 1.0
+          run_name: !!python/object:omegaconf.nodes.AnyNode
+            _metadata: !!python/object:omegaconf.base.Metadata
+              flags: {}
+              flags_root: false
+              key: run_name
+              object_type: null
+              optional: true
+              ref_type: *id002
+              resolver_cache: !!python/object/apply:collections.defaultdict
+              - *id003
+            _parent: *id009
+            _val: pretrain_hybrid_sudoku
+          seed: !!python/object:omegaconf.nodes.AnyNode
+            _metadata: !!python/object:omegaconf.base.Metadata
+              flags: {}
+              flags_root: false
+              key: seed
+              object_type: null
+              optional: true
+              ref_type: *id002
+              resolver_cache: !!python/object/apply:collections.defaultdict
+              - *id003
+            _parent: *id009
+            _val: 0
+          weight_decay: !!python/object:omegaconf.nodes.AnyNode
+            _metadata: !!python/object:omegaconf.base.Metadata
+              flags: {}
+              flags_root: false
+              key: weight_decay
+              object_type: null
+              optional: true
+              ref_type: *id002
+              resolver_cache: !!python/object/apply:collections.defaultdict
+              - *id003
+            _parent: *id009
+            _val: 1.0
+        _metadata: !!python/object:omegaconf.base.ContainerMetadata
+          element_type: *id002
+          flags:
+            struct: true
+          flags_root: false
+          key: null
+          key_type: *id002
+          object_type: *id003
+          optional: true
+          ref_type: *id002
+          resolver_cache: !!python/object/apply:collections.defaultdict
+            args:
+            - *id003
+            dictitems:
+              now:
+                ? !!python/tuple
+                - '%H-%M-%S'
+                : 09-39-56
+                ? !!python/tuple
+                - '%Y-%m-%d'
+                : '2025-10-11'
+        _parent: null
+  ffn_kron_out_factors: *id005
+  ffn_kron_share_gate_up: true
+  ffn_kron_terms: 96
+  ffn_rank: 128
+  ffn_type: mlp
+  forward_dtype: bfloat16
+  halt_exploration_prob: 0.1
+  halt_max_steps: 16
+  hidden_size: 512
+  loss:
+    loss_type: stablemax_cross_entropy
+    name: losses@ACTLossHead
+  mixer: attention
+  mlp_t: true
+  name: recursive_reasoning.trm@TinyRecursiveReasoningModel_ACTV1
+  no_ACT_continue: true
+  num_heads: 8
+  pos_encodings: rope
+  puzzle_emb_len: 16
+  puzzle_emb_ndim: 512
+beta1: 0.9
+beta2: 0.95
+checkpoint_every_eval: true
+checkpoint_path: checkpoints/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_hybrid_sudoku
+data_paths:
+- data/sudoku-extreme-1k-aug-1000
+data_paths_test: []
+ema: true
+ema_rate: 0.999
+epochs: 50000
+eval_interval: 5000
+eval_save_outputs: []
+evaluators: []
+freeze_weights: false
+global_batch_size: 768
+load_checkpoint: null
+lr: 0.0001
+lr_min_ratio: 1.0
+lr_warmup_steps: 2000
+min_eval_interval: 0
+project_name: Sudoku-extreme-1k-aug-1000-ACT-torch
+puzzle_emb_lr: 0.0001
+puzzle_emb_weight_decay: 1.0
+run_name: pretrain_hybrid_sudoku
+seed: 0
+weight_decay: 1.0
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_hybrid_sudoku/losses.py b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_hybrid_sudoku/losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..2597cd67cc4530c6536fd1c479a60ce6dea94b79
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_hybrid_sudoku/losses.py
@@ -0,0 +1,103 @@
+from typing import Any, Tuple, Dict, Sequence, Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+import math
+
+IGNORE_LABEL_ID = -100
+
+
+def s(x, epsilon=1e-30):
+    return torch.where(
+        x<0,
+        1/(1-x+ epsilon),
+        x + 1
+    )
+
+
+def log_stablemax(x, dim=-1):
+    s_x = s(x)
+    return torch.log(s_x/torch.sum(s_x, dim=dim, keepdim=True))
+
+
+def stablemax_cross_entropy(logits, labels, ignore_index: int = -100, valid_mask=None):
+    logprobs = log_stablemax(logits.to(torch.float64), dim=-1)
+
+    if valid_mask is None:
+        valid_mask = (labels != ignore_index)
+    transformed_labels = torch.where(valid_mask, labels, 0)
+    prediction_logprobs = torch.gather(logprobs, index=transformed_labels.to(torch.long).unsqueeze(-1), dim=-1).squeeze(-1)
+
+    return -torch.where(valid_mask, prediction_logprobs, 0)
+
+
+def softmax_cross_entropy(logits, labels, ignore_index: int = -100):
+    # Cast logits to f32
+    # Flatten logits
+    return F.cross_entropy(logits.to(torch.float32).view(-1, logits.shape[-1]), labels.to(torch.long).view(-1), ignore_index=ignore_index, reduction="none").view(labels.shape)
+
+
+class ACTLossHead(nn.Module):
+    def __init__(self, model: nn.Module, loss_type: str):
+        super().__init__()
+        self.model = model
+        self.loss_fn = globals()[loss_type]
+        
+    def initial_carry(self, *args, **kwargs):
+        return self.model.initial_carry(*args, **kwargs)  # type: ignore
+
+    def forward(
+        self,
+        return_keys: Sequence[str],
+        # Model args
+        **model_kwargs,
+    ) -> Tuple[Any, torch.Tensor, Dict[str, torch.Tensor], Optional[Dict[str, torch.Tensor]], torch.Tensor]:
+        # Model logits
+        # B x SeqLen x D
+        new_carry, outputs = self.model(**model_kwargs)
+        labels = new_carry.current_data["labels"]
+
+        with torch.no_grad():
+            # Preds
+            outputs["preds"] = torch.argmax(outputs["logits"], dim=-1)
+
+            # Correctness
+            mask = (labels != IGNORE_LABEL_ID)
+            loss_counts = mask.sum(-1)
+            loss_divisor = loss_counts.clamp_min(1).unsqueeze(-1)  # Avoid NaNs in division
+
+            is_correct = mask & (torch.argmax(outputs["logits"], dim=-1) == labels)
+            seq_is_correct = is_correct.sum(-1) == loss_counts
+            
+            # Metrics (halted)
+            valid_metrics = new_carry.halted & (loss_counts > 0)
+            metrics = {
+                "count": valid_metrics.sum(),
+                
+                "accuracy":       torch.where(valid_metrics, (is_correct.to(torch.float32) / loss_divisor).sum(-1), 0).sum(),
+                "exact_accuracy": (valid_metrics & seq_is_correct).sum(),
+
+                "q_halt_accuracy": (valid_metrics & ((outputs["q_halt_logits"] >= 0) == seq_is_correct)).sum(),
+                "steps":          torch.where(valid_metrics, new_carry.steps, 0).sum(),
+            }
+
+        # Losses
+
+        lm_loss = (self.loss_fn(outputs["logits"], labels, ignore_index=IGNORE_LABEL_ID, valid_mask=mask) / loss_divisor).sum()
+        q_halt_loss = F.binary_cross_entropy_with_logits(outputs["q_halt_logits"], seq_is_correct.to(outputs["q_halt_logits"].dtype), reduction="sum")
+        metrics.update({
+            "lm_loss": lm_loss.detach(),
+            "q_halt_loss": q_halt_loss.detach(),
+        })
+        # Q continue (bootstrapping target loss); Alexia: This fits Q-learning, but seems totally unecessary
+        q_continue_loss = 0
+        if "target_q_continue" in outputs:
+            q_continue_loss = F.binary_cross_entropy_with_logits(outputs["q_continue_logits"], outputs["target_q_continue"], reduction="sum")
+
+            metrics["q_continue_loss"] = q_continue_loss.detach()
+        # Filter outputs for return
+        detached_outputs = {k: outputs[k].detach() for k in return_keys if k in outputs}
+
+        return new_carry, lm_loss + 0.5 * (q_halt_loss + q_continue_loss), metrics, detached_outputs, new_carry.halted.all()
+
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_hybrid_sudoku/trm.py b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_hybrid_sudoku/trm.py
new file mode 100644
index 0000000000000000000000000000000000000000..41c30ed4b7cd80c7eeacd142729da3800a54535e
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_hybrid_sudoku/trm.py
@@ -0,0 +1,357 @@
+from typing import Tuple, List, Dict, Optional
+from dataclasses import dataclass
+import math
+import torch
+import copy
+import torch.nn.functional as F
+from torch import nn
+from pydantic import BaseModel
+import random
+from models.common import trunc_normal_init_
+from models.layers import rms_norm, LinearSwish, SwiGLU, Attention, RotaryEmbedding, CosSin, CastedEmbedding, CastedLinear
+from models.sparse_embedding import CastedSparseEmbedding
+
+IGNORE_LABEL_ID = -100
+
+@dataclass
+class TinyRecursiveReasoningModel_ACTV1InnerCarry:
+    z_H: torch.Tensor
+    z_L: torch.Tensor
+
+
+@dataclass
+class TinyRecursiveReasoningModel_ACTV1Carry:
+    inner_carry: TinyRecursiveReasoningModel_ACTV1InnerCarry
+    
+    steps: torch.Tensor
+    halted: torch.Tensor
+    
+    current_data: Dict[str, torch.Tensor]
+
+
+class TinyRecursiveReasoningModel_ACTV1Config(BaseModel):
+    batch_size: int
+    seq_len: int
+    puzzle_emb_ndim: int = 0
+    num_puzzle_identifiers: int
+    vocab_size: int
+
+    H_cycles: int
+    L_cycles: int
+
+    H_layers: int # ignored
+    L_layers: int
+
+    # Transformer config
+    hidden_size: int
+    expansion: float
+    num_heads: int
+    pos_encodings: str
+
+    rms_norm_eps: float = 1e-5
+    rope_theta: float = 10000.0
+    
+    # Halting Q-learning config
+    halt_max_steps: int
+    halt_exploration_prob: float
+
+    forward_dtype: str = "bfloat16"
+
+    # Alexia: added
+    mlp_t: bool = False # use mlp on L instead of transformer
+    puzzle_emb_len: int = 16 # if non-zero, its specified to this value
+    no_ACT_continue: bool =  True # No continue ACT loss, only use the sigmoid of the halt which makes much more sense
+    # FFN low-rank options (default off)
+    ffn_rank: Optional[int] = None
+    ffn_sandwich_k: int = 6
+    ffn_use_structured: bool = True
+    # FFN: Kronecker (default off)
+    ffn_kron_terms: Optional[int] = None
+    ffn_kron_in_factors: Optional[Tuple[int, int]] = None
+    ffn_kron_out_factors: Optional[Tuple[int, int]] = None
+    ffn_kron_share_gate_up: bool = False
+    # FFN: Hybrid Kron + Low-rank (default off)
+    ffn_hybrid: bool = False
+    ffn_hybrid_gate: str = 'scalar'
+    # Kron streaming knobs
+    ffn_kron_chunk_k: int = 16
+    ffn_kron_chunk_t: int = 4096
+
+class TinyRecursiveReasoningModel_ACTV1Block(nn.Module):
+    def __init__(self, config: TinyRecursiveReasoningModel_ACTV1Config) -> None:
+        super().__init__()
+
+        self.config = config
+        if self.config.mlp_t:
+            self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size) if self.config.puzzle_emb_len == 0 else self.config.puzzle_emb_len
+            self.mlp_t = SwiGLU(
+                hidden_size=self.config.seq_len + self.puzzle_emb_len, # L
+                expansion=config.expansion,
+            )
+        else:
+            self.self_attn = Attention(
+                hidden_size=config.hidden_size,
+                head_dim=config.hidden_size // config.num_heads,
+                num_heads=config.num_heads,
+                num_key_value_heads=config.num_heads,
+                causal=False
+            )
+        # FFN selection precedence: Hybrid > Kron > Low-rank > Dense
+        if bool(getattr(self.config, 'ffn_hybrid', False)) \
+           and ((getattr(self.config, 'ffn_kron_terms', 0) or 0) > 0) \
+           and ((getattr(self.config, 'ffn_rank', 0) or 0) > 0):
+            from models.layers import SwiGLU_KronLowRankHybrid
+            self.mlp = SwiGLU_KronLowRankHybrid(
+                hidden_size=config.hidden_size,
+                expansion=config.expansion,
+                k_terms=int(self.config.ffn_kron_terms),
+                rank=int(self.config.ffn_rank),
+                in_factors=getattr(self.config, 'ffn_kron_in_factors', None),
+                out_factors=getattr(self.config, 'ffn_kron_out_factors', None),
+                share_gate_up=bool(getattr(self.config, 'ffn_kron_share_gate_up', False)),
+                gate_mode=str(getattr(self.config, 'ffn_hybrid_gate', 'scalar')),
+                chunk_k=int(getattr(self.config, 'ffn_kron_chunk_k', 8)),
+                chunk_t=int(getattr(self.config, 'ffn_kron_chunk_t', 8192)),
+            )
+        elif (getattr(self.config, 'ffn_kron_terms', None) is not None) and (int(self.config.ffn_kron_terms or 0) > 0):
+            from models.layers import SwiGLU_Kron
+            self.mlp = SwiGLU_Kron(
+                hidden_size=config.hidden_size,
+                expansion=config.expansion,
+                k_terms=int(self.config.ffn_kron_terms),
+                in_factors=getattr(self.config, 'ffn_kron_in_factors', None),
+                out_factors=getattr(self.config, 'ffn_kron_out_factors', None),
+                share_gate_up=bool(getattr(self.config, 'ffn_kron_share_gate_up', False)),
+                bias=False,
+                chunk_k=int(getattr(self.config, 'ffn_kron_chunk_k', 8)),
+                chunk_t=int(getattr(self.config, 'ffn_kron_chunk_t', 8192)),
+            )
+        elif (self.config.ffn_rank is not None) and (self.config.ffn_rank > 0):
+            # Import locally to avoid circulars
+            from models.layers import SwiGLU_LowRankStructured
+            self.mlp = SwiGLU_LowRankStructured(
+                hidden_size=config.hidden_size,
+                expansion=config.expansion,
+                rank=int(self.config.ffn_rank),
+                k_house=int(self.config.ffn_sandwich_k),
+                use_structured=bool(self.config.ffn_use_structured),
+            )
+        else:
+            self.mlp = SwiGLU(
+                hidden_size=config.hidden_size,
+                expansion=config.expansion,
+            )
+        self.norm_eps = config.rms_norm_eps
+
+    def forward(self, cos_sin: CosSin, hidden_states: torch.Tensor) -> torch.Tensor:
+        # B, L, D = hidden_states.shape
+        # Post Norm
+        if self.config.mlp_t:
+            hidden_states = hidden_states.transpose(1,2)
+            out = self.mlp_t(hidden_states)
+            hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+            hidden_states = hidden_states.transpose(1,2)
+        else:
+            # Self Attention
+            hidden_states = rms_norm(hidden_states + self.self_attn(cos_sin=cos_sin, hidden_states=hidden_states), variance_epsilon=self.norm_eps)
+        # Fully Connected (activation checkpointing for memory savings)
+        try:
+            from torch.utils.checkpoint import checkpoint
+            out = checkpoint(self.mlp, hidden_states, use_reentrant=False)
+        except Exception:
+            out = self.mlp(hidden_states)
+        hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+        return hidden_states
+
+class TinyRecursiveReasoningModel_ACTV1ReasoningModule(nn.Module):
+    def __init__(self, layers: List[TinyRecursiveReasoningModel_ACTV1Block]):
+        super().__init__()
+        self.layers = torch.nn.ModuleList(layers)
+
+    def forward(self, hidden_states: torch.Tensor, input_injection: torch.Tensor, **kwargs) -> torch.Tensor:
+        hidden_states = hidden_states + input_injection
+        for layer in self.layers:
+            hidden_states = layer(hidden_states=hidden_states, **kwargs)
+        return hidden_states
+
+
+class TinyRecursiveReasoningModel_ACTV1_Inner(nn.Module):
+    def __init__(self, config: TinyRecursiveReasoningModel_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        self.forward_dtype = getattr(torch, self.config.forward_dtype)
+
+        # I/O
+
+        self.embed_scale = math.sqrt(self.config.hidden_size)
+        embed_init_std = 1.0 / self.embed_scale
+
+        self.embed_tokens = CastedEmbedding(self.config.vocab_size, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+        self.lm_head      = CastedLinear(self.config.hidden_size, self.config.vocab_size, bias=False)
+        self.q_head       = CastedLinear(self.config.hidden_size, 2, bias=True)
+
+        self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size)  if self.config.puzzle_emb_len == 0 else self.config.puzzle_emb_len  # ceil div
+        if self.config.puzzle_emb_ndim > 0:
+            # Zero init puzzle embeddings
+            self.puzzle_emb = CastedSparseEmbedding(self.config.num_puzzle_identifiers, self.config.puzzle_emb_ndim,
+                                                    batch_size=self.config.batch_size, init_std=0, cast_to=self.forward_dtype)
+
+        # LM Blocks
+        if self.config.pos_encodings == "rope":
+            self.rotary_emb = RotaryEmbedding(dim=self.config.hidden_size // self.config.num_heads,
+                                              max_position_embeddings=self.config.seq_len + self.puzzle_emb_len,
+                                              base=self.config.rope_theta)
+        elif self.config.pos_encodings == "learned":
+            self.embed_pos = CastedEmbedding(self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+        else:
+            pass
+
+        # Reasoning Layers
+        self.L_level = TinyRecursiveReasoningModel_ACTV1ReasoningModule(layers=[TinyRecursiveReasoningModel_ACTV1Block(self.config) for _i in range(self.config.L_layers)])
+
+        # Initial states
+        self.H_init = nn.Buffer(trunc_normal_init_(torch.empty(self.config.hidden_size, dtype=self.forward_dtype), std=1), persistent=True)
+        self.L_init = nn.Buffer(trunc_normal_init_(torch.empty(self.config.hidden_size, dtype=self.forward_dtype), std=1), persistent=True)
+
+        # Q head special init
+        # Init Q to (almost) zero for faster learning during bootstrapping
+        with torch.no_grad():
+            self.q_head.weight.zero_()
+            self.q_head.bias.fill_(-5)  # type: ignore
+
+    def _input_embeddings(self, input: torch.Tensor, puzzle_identifiers: torch.Tensor):
+        # Token embedding
+        embedding = self.embed_tokens(input.to(torch.int32))
+
+        # Puzzle embeddings
+        if self.config.puzzle_emb_ndim > 0:
+            puzzle_embedding = self.puzzle_emb(puzzle_identifiers)
+            
+            pad_count = self.puzzle_emb_len * self.config.hidden_size - puzzle_embedding.shape[-1]
+            if pad_count > 0:
+                puzzle_embedding = F.pad(puzzle_embedding, (0, pad_count))
+
+            embedding = torch.cat((puzzle_embedding.view(-1, self.puzzle_emb_len, self.config.hidden_size), embedding), dim=-2)
+
+        # Position embeddings
+        if self.config.pos_encodings == "learned":
+            # scale by 1/sqrt(2) to maintain forward variance
+            embedding = 0.707106781 * (embedding + self.embed_pos.embedding_weight.to(self.forward_dtype))
+
+        # Scale
+        return self.embed_scale * embedding
+
+    def empty_carry(self, batch_size: int):
+        return TinyRecursiveReasoningModel_ACTV1InnerCarry(
+            z_H=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+            z_L=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+        )
+        
+    def reset_carry(self, reset_flag: torch.Tensor, carry: TinyRecursiveReasoningModel_ACTV1InnerCarry):
+        return TinyRecursiveReasoningModel_ACTV1InnerCarry(
+            z_H=torch.where(reset_flag.view(-1, 1, 1), self.H_init, carry.z_H),
+            z_L=torch.where(reset_flag.view(-1, 1, 1), self.L_init, carry.z_L),
+        )
+
+    def forward(self, carry: TinyRecursiveReasoningModel_ACTV1InnerCarry, batch: Dict[str, torch.Tensor]) -> Tuple[TinyRecursiveReasoningModel_ACTV1InnerCarry, torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        seq_info = dict(
+            cos_sin=self.rotary_emb() if hasattr(self, "rotary_emb") else None,
+        )
+
+        # Input encoding
+        input_embeddings = self._input_embeddings(batch["inputs"], batch["puzzle_identifiers"])
+
+        # Forward iterations
+        it = 0
+        z_H, z_L = carry.z_H, carry.z_L
+        # H_cycles-1 without grad
+        with torch.no_grad():
+            for _H_step in range(self.config.H_cycles-1):
+                for _L_step in range(self.config.L_cycles):
+                    z_L = self.L_level(z_L, z_H + input_embeddings, **seq_info)
+                z_H = self.L_level(z_H, z_L, **seq_info)
+        # 1 with grad
+        for _L_step in range(self.config.L_cycles):
+            z_L = self.L_level(z_L, z_H + input_embeddings, **seq_info)
+        z_H = self.L_level(z_H, z_L, **seq_info)
+
+        # LM Outputs
+        new_carry = TinyRecursiveReasoningModel_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach())  # New carry no grad
+        output = self.lm_head(z_H)[:, self.puzzle_emb_len:]
+        q_logits = self.q_head(z_H[:, 0]).to(torch.float32) # Q-head; uses the first puzzle_emb position
+        return new_carry, output, (q_logits[..., 0], q_logits[..., 1])
+
+
+class TinyRecursiveReasoningModel_ACTV1(nn.Module):
+    """ACT wrapper."""
+
+    def __init__(self, config_dict: dict):
+        super().__init__()
+        self.config = TinyRecursiveReasoningModel_ACTV1Config(**config_dict)
+        self.inner = TinyRecursiveReasoningModel_ACTV1_Inner(self.config)
+
+    @property
+    def puzzle_emb(self):
+        return self.inner.puzzle_emb
+
+    def initial_carry(self, batch: Dict[str, torch.Tensor]):
+        batch_size = batch["inputs"].shape[0]
+
+        return TinyRecursiveReasoningModel_ACTV1Carry(
+            inner_carry=self.inner.empty_carry(batch_size),  # Empty is expected, it will be reseted in first pass as all sequences are halted.
+            
+            steps=torch.zeros((batch_size, ), dtype=torch.int32),
+            halted=torch.ones((batch_size, ), dtype=torch.bool),  # Default to halted
+            
+            current_data={k: torch.empty_like(v) for k, v in batch.items()}
+        )
+        
+    def forward(self, carry: TinyRecursiveReasoningModel_ACTV1Carry, batch: Dict[str, torch.Tensor]) -> Tuple[TinyRecursiveReasoningModel_ACTV1Carry, Dict[str, torch.Tensor]]:
+
+        # Update data, carry (removing halted sequences)
+        new_inner_carry = self.inner.reset_carry(carry.halted, carry.inner_carry)
+        
+        new_steps = torch.where(carry.halted, 0, carry.steps)
+
+        new_current_data = {k: torch.where(carry.halted.view((-1, ) + (1, ) * (batch[k].ndim - 1)), batch[k], v) for k, v in carry.current_data.items()}
+
+        # Forward inner model
+        new_inner_carry, logits, (q_halt_logits, q_continue_logits) = self.inner(new_inner_carry, new_current_data)
+
+        outputs = {
+            "logits": logits,
+            "q_halt_logits": q_halt_logits,
+            "q_continue_logits": q_continue_logits
+        }
+
+        with torch.no_grad():
+            # Step
+            new_steps = new_steps + 1
+            is_last_step = new_steps >= self.config.halt_max_steps
+            
+            halted = is_last_step
+
+            # if training, and ACT is enabled
+            if self.training and (self.config.halt_max_steps > 1):
+
+                # Halt signal
+                # NOTE: During evaluation, always use max steps, this is to guarantee the same halting steps inside a batch for batching purposes
+                
+                if self.config.no_ACT_continue:
+                    halted = halted | (q_halt_logits > 0)
+                else:
+                    halted = halted | (q_halt_logits > q_continue_logits)
+
+                # Exploration
+                min_halt_steps = (torch.rand_like(q_halt_logits) < self.config.halt_exploration_prob) * torch.randint_like(new_steps, low=2, high=self.config.halt_max_steps + 1)
+                halted = halted & (new_steps >= min_halt_steps)
+
+                if not self.config.no_ACT_continue:
+                    # Compute target Q
+                    # NOTE: No replay buffer and target networks for computing target Q-value.
+                    # As batch_size is large, there're many parallel envs.
+                    # Similar concept as PQN https://arxiv.org/abs/2407.04811
+                    _, _, (next_q_halt_logits, next_q_continue_logits), _, _ = self.inner(new_inner_carry, new_current_data)
+                    outputs["target_q_continue"] = torch.sigmoid(torch.where(is_last_step, next_q_halt_logits, torch.maximum(next_q_halt_logits, next_q_continue_logits)))
+
+        return TinyRecursiveReasoningModel_ACTV1Carry(new_inner_carry, new_steps, halted, new_current_data), outputs
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_lowrank_sudoku/all_config.yaml b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_lowrank_sudoku/all_config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..6a6ff868eec4f5f434639bfcb902eca757016208
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_lowrank_sudoku/all_config.yaml
@@ -0,0 +1,51 @@
+arch:
+  H_cycles: 3
+  H_layers: 0
+  L_cycles: 6
+  L_layers: 2
+  expansion: 4
+  ffn_rank: 224
+  ffn_sandwich_k: 0
+  ffn_type: mlp
+  ffn_use_structured: false
+  forward_dtype: bfloat16
+  halt_exploration_prob: 0.1
+  halt_max_steps: 16
+  hidden_size: 512
+  loss:
+    loss_type: stablemax_cross_entropy
+    name: losses@ACTLossHead
+  mixer: attention
+  mlp_t: true
+  name: recursive_reasoning.trm@TinyRecursiveReasoningModel_ACTV1
+  no_ACT_continue: true
+  num_heads: 8
+  pos_encodings: rope
+  puzzle_emb_len: 16
+  puzzle_emb_ndim: 512
+beta1: 0.9
+beta2: 0.95
+checkpoint_every_eval: true
+checkpoint_path: checkpoints/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_lowrank_sudoku
+data_paths:
+- data/sudoku-extreme-1k-aug-1000
+data_paths_test: []
+ema: true
+ema_rate: 0.999
+epochs: 50000
+eval_interval: 5000
+eval_save_outputs: []
+evaluators: []
+freeze_weights: false
+global_batch_size: 768
+load_checkpoint: null
+lr: 0.0001
+lr_min_ratio: 1.0
+lr_warmup_steps: 2000
+min_eval_interval: 0
+project_name: Sudoku-extreme-1k-aug-1000-ACT-torch
+puzzle_emb_lr: 0.0001
+puzzle_emb_weight_decay: 1.0
+run_name: pretrain_lowrank_sudoku
+seed: 0
+weight_decay: 1.0
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_lowrank_sudoku/losses.py b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_lowrank_sudoku/losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..2597cd67cc4530c6536fd1c479a60ce6dea94b79
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_lowrank_sudoku/losses.py
@@ -0,0 +1,103 @@
+from typing import Any, Tuple, Dict, Sequence, Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+import math
+
+IGNORE_LABEL_ID = -100
+
+
+def s(x, epsilon=1e-30):
+    return torch.where(
+        x<0,
+        1/(1-x+ epsilon),
+        x + 1
+    )
+
+
+def log_stablemax(x, dim=-1):
+    s_x = s(x)
+    return torch.log(s_x/torch.sum(s_x, dim=dim, keepdim=True))
+
+
+def stablemax_cross_entropy(logits, labels, ignore_index: int = -100, valid_mask=None):
+    logprobs = log_stablemax(logits.to(torch.float64), dim=-1)
+
+    if valid_mask is None:
+        valid_mask = (labels != ignore_index)
+    transformed_labels = torch.where(valid_mask, labels, 0)
+    prediction_logprobs = torch.gather(logprobs, index=transformed_labels.to(torch.long).unsqueeze(-1), dim=-1).squeeze(-1)
+
+    return -torch.where(valid_mask, prediction_logprobs, 0)
+
+
+def softmax_cross_entropy(logits, labels, ignore_index: int = -100):
+    # Cast logits to f32
+    # Flatten logits
+    return F.cross_entropy(logits.to(torch.float32).view(-1, logits.shape[-1]), labels.to(torch.long).view(-1), ignore_index=ignore_index, reduction="none").view(labels.shape)
+
+
+class ACTLossHead(nn.Module):
+    def __init__(self, model: nn.Module, loss_type: str):
+        super().__init__()
+        self.model = model
+        self.loss_fn = globals()[loss_type]
+        
+    def initial_carry(self, *args, **kwargs):
+        return self.model.initial_carry(*args, **kwargs)  # type: ignore
+
+    def forward(
+        self,
+        return_keys: Sequence[str],
+        # Model args
+        **model_kwargs,
+    ) -> Tuple[Any, torch.Tensor, Dict[str, torch.Tensor], Optional[Dict[str, torch.Tensor]], torch.Tensor]:
+        # Model logits
+        # B x SeqLen x D
+        new_carry, outputs = self.model(**model_kwargs)
+        labels = new_carry.current_data["labels"]
+
+        with torch.no_grad():
+            # Preds
+            outputs["preds"] = torch.argmax(outputs["logits"], dim=-1)
+
+            # Correctness
+            mask = (labels != IGNORE_LABEL_ID)
+            loss_counts = mask.sum(-1)
+            loss_divisor = loss_counts.clamp_min(1).unsqueeze(-1)  # Avoid NaNs in division
+
+            is_correct = mask & (torch.argmax(outputs["logits"], dim=-1) == labels)
+            seq_is_correct = is_correct.sum(-1) == loss_counts
+            
+            # Metrics (halted)
+            valid_metrics = new_carry.halted & (loss_counts > 0)
+            metrics = {
+                "count": valid_metrics.sum(),
+                
+                "accuracy":       torch.where(valid_metrics, (is_correct.to(torch.float32) / loss_divisor).sum(-1), 0).sum(),
+                "exact_accuracy": (valid_metrics & seq_is_correct).sum(),
+
+                "q_halt_accuracy": (valid_metrics & ((outputs["q_halt_logits"] >= 0) == seq_is_correct)).sum(),
+                "steps":          torch.where(valid_metrics, new_carry.steps, 0).sum(),
+            }
+
+        # Losses
+
+        lm_loss = (self.loss_fn(outputs["logits"], labels, ignore_index=IGNORE_LABEL_ID, valid_mask=mask) / loss_divisor).sum()
+        q_halt_loss = F.binary_cross_entropy_with_logits(outputs["q_halt_logits"], seq_is_correct.to(outputs["q_halt_logits"].dtype), reduction="sum")
+        metrics.update({
+            "lm_loss": lm_loss.detach(),
+            "q_halt_loss": q_halt_loss.detach(),
+        })
+        # Q continue (bootstrapping target loss); Alexia: This fits Q-learning, but seems totally unecessary
+        q_continue_loss = 0
+        if "target_q_continue" in outputs:
+            q_continue_loss = F.binary_cross_entropy_with_logits(outputs["q_continue_logits"], outputs["target_q_continue"], reduction="sum")
+
+            metrics["q_continue_loss"] = q_continue_loss.detach()
+        # Filter outputs for return
+        detached_outputs = {k: outputs[k].detach() for k in return_keys if k in outputs}
+
+        return new_carry, lm_loss + 0.5 * (q_halt_loss + q_continue_loss), metrics, detached_outputs, new_carry.halted.all()
+
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_lowrank_sudoku/trm.py b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_lowrank_sudoku/trm.py
new file mode 100644
index 0000000000000000000000000000000000000000..86bb19bb1109c1caf7dca0f2f26f805e925169e6
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_lowrank_sudoku/trm.py
@@ -0,0 +1,312 @@
+from typing import Tuple, List, Dict, Optional
+from dataclasses import dataclass
+import math
+import torch
+import copy
+import torch.nn.functional as F
+from torch import nn
+from pydantic import BaseModel
+import random
+from models.common import trunc_normal_init_
+from models.layers import rms_norm, LinearSwish, SwiGLU, Attention, RotaryEmbedding, CosSin, CastedEmbedding, CastedLinear
+from models.sparse_embedding import CastedSparseEmbedding
+
+IGNORE_LABEL_ID = -100
+
+@dataclass
+class TinyRecursiveReasoningModel_ACTV1InnerCarry:
+    z_H: torch.Tensor
+    z_L: torch.Tensor
+
+
+@dataclass
+class TinyRecursiveReasoningModel_ACTV1Carry:
+    inner_carry: TinyRecursiveReasoningModel_ACTV1InnerCarry
+    
+    steps: torch.Tensor
+    halted: torch.Tensor
+    
+    current_data: Dict[str, torch.Tensor]
+
+
+class TinyRecursiveReasoningModel_ACTV1Config(BaseModel):
+    batch_size: int
+    seq_len: int
+    puzzle_emb_ndim: int = 0
+    num_puzzle_identifiers: int
+    vocab_size: int
+
+    H_cycles: int
+    L_cycles: int
+
+    H_layers: int # ignored
+    L_layers: int
+
+    # Transformer config
+    hidden_size: int
+    expansion: float
+    num_heads: int
+    pos_encodings: str
+
+    rms_norm_eps: float = 1e-5
+    rope_theta: float = 10000.0
+    
+    # Halting Q-learning config
+    halt_max_steps: int
+    halt_exploration_prob: float
+
+    forward_dtype: str = "bfloat16"
+
+    # Alexia: added
+    mlp_t: bool = False # use mlp on L instead of transformer
+    puzzle_emb_len: int = 16 # if non-zero, its specified to this value
+    no_ACT_continue: bool =  True # No continue ACT loss, only use the sigmoid of the halt which makes much more sense
+    # FFN low-rank options (default off)
+    ffn_rank: Optional[int] = None
+    ffn_sandwich_k: int = 6
+    ffn_use_structured: bool = True
+
+class TinyRecursiveReasoningModel_ACTV1Block(nn.Module):
+    def __init__(self, config: TinyRecursiveReasoningModel_ACTV1Config) -> None:
+        super().__init__()
+
+        self.config = config
+        if self.config.mlp_t:
+            self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size) if self.config.puzzle_emb_len == 0 else self.config.puzzle_emb_len
+            self.mlp_t = SwiGLU(
+                hidden_size=self.config.seq_len + self.puzzle_emb_len, # L
+                expansion=config.expansion,
+            )
+        else:
+            self.self_attn = Attention(
+                hidden_size=config.hidden_size,
+                head_dim=config.hidden_size // config.num_heads,
+                num_heads=config.num_heads,
+                num_key_value_heads=config.num_heads,
+                causal=False
+            )
+        if (self.config.ffn_rank is not None) and (self.config.ffn_rank > 0):
+            # Import locally to avoid circulars
+            from models.layers import SwiGLU_LowRankStructured
+            self.mlp = SwiGLU_LowRankStructured(
+                hidden_size=config.hidden_size,
+                expansion=config.expansion,
+                rank=int(self.config.ffn_rank),
+                k_house=int(self.config.ffn_sandwich_k),
+                use_structured=bool(self.config.ffn_use_structured),
+            )
+        else:
+            self.mlp = SwiGLU(
+                hidden_size=config.hidden_size,
+                expansion=config.expansion,
+            )
+        self.norm_eps = config.rms_norm_eps
+
+    def forward(self, cos_sin: CosSin, hidden_states: torch.Tensor) -> torch.Tensor:
+        # B, L, D = hidden_states.shape
+        # Post Norm
+        if self.config.mlp_t:
+            hidden_states = hidden_states.transpose(1,2)
+            out = self.mlp_t(hidden_states)
+            hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+            hidden_states = hidden_states.transpose(1,2)
+        else:
+            # Self Attention
+            hidden_states = rms_norm(hidden_states + self.self_attn(cos_sin=cos_sin, hidden_states=hidden_states), variance_epsilon=self.norm_eps)
+        # Fully Connected
+        out = self.mlp(hidden_states)
+        hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+        return hidden_states
+
+class TinyRecursiveReasoningModel_ACTV1ReasoningModule(nn.Module):
+    def __init__(self, layers: List[TinyRecursiveReasoningModel_ACTV1Block]):
+        super().__init__()
+        self.layers = torch.nn.ModuleList(layers)
+
+    def forward(self, hidden_states: torch.Tensor, input_injection: torch.Tensor, **kwargs) -> torch.Tensor:
+        hidden_states = hidden_states + input_injection
+        for layer in self.layers:
+            hidden_states = layer(hidden_states=hidden_states, **kwargs)
+        return hidden_states
+
+
+class TinyRecursiveReasoningModel_ACTV1_Inner(nn.Module):
+    def __init__(self, config: TinyRecursiveReasoningModel_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        self.forward_dtype = getattr(torch, self.config.forward_dtype)
+
+        # I/O
+
+        self.embed_scale = math.sqrt(self.config.hidden_size)
+        embed_init_std = 1.0 / self.embed_scale
+
+        self.embed_tokens = CastedEmbedding(self.config.vocab_size, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+        self.lm_head      = CastedLinear(self.config.hidden_size, self.config.vocab_size, bias=False)
+        self.q_head       = CastedLinear(self.config.hidden_size, 2, bias=True)
+
+        self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size)  if self.config.puzzle_emb_len == 0 else self.config.puzzle_emb_len  # ceil div
+        if self.config.puzzle_emb_ndim > 0:
+            # Zero init puzzle embeddings
+            self.puzzle_emb = CastedSparseEmbedding(self.config.num_puzzle_identifiers, self.config.puzzle_emb_ndim,
+                                                    batch_size=self.config.batch_size, init_std=0, cast_to=self.forward_dtype)
+
+        # LM Blocks
+        if self.config.pos_encodings == "rope":
+            self.rotary_emb = RotaryEmbedding(dim=self.config.hidden_size // self.config.num_heads,
+                                              max_position_embeddings=self.config.seq_len + self.puzzle_emb_len,
+                                              base=self.config.rope_theta)
+        elif self.config.pos_encodings == "learned":
+            self.embed_pos = CastedEmbedding(self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+        else:
+            pass
+
+        # Reasoning Layers
+        self.L_level = TinyRecursiveReasoningModel_ACTV1ReasoningModule(layers=[TinyRecursiveReasoningModel_ACTV1Block(self.config) for _i in range(self.config.L_layers)])
+
+        # Initial states
+        self.H_init = nn.Buffer(trunc_normal_init_(torch.empty(self.config.hidden_size, dtype=self.forward_dtype), std=1), persistent=True)
+        self.L_init = nn.Buffer(trunc_normal_init_(torch.empty(self.config.hidden_size, dtype=self.forward_dtype), std=1), persistent=True)
+
+        # Q head special init
+        # Init Q to (almost) zero for faster learning during bootstrapping
+        with torch.no_grad():
+            self.q_head.weight.zero_()
+            self.q_head.bias.fill_(-5)  # type: ignore
+
+    def _input_embeddings(self, input: torch.Tensor, puzzle_identifiers: torch.Tensor):
+        # Token embedding
+        embedding = self.embed_tokens(input.to(torch.int32))
+
+        # Puzzle embeddings
+        if self.config.puzzle_emb_ndim > 0:
+            puzzle_embedding = self.puzzle_emb(puzzle_identifiers)
+            
+            pad_count = self.puzzle_emb_len * self.config.hidden_size - puzzle_embedding.shape[-1]
+            if pad_count > 0:
+                puzzle_embedding = F.pad(puzzle_embedding, (0, pad_count))
+
+            embedding = torch.cat((puzzle_embedding.view(-1, self.puzzle_emb_len, self.config.hidden_size), embedding), dim=-2)
+
+        # Position embeddings
+        if self.config.pos_encodings == "learned":
+            # scale by 1/sqrt(2) to maintain forward variance
+            embedding = 0.707106781 * (embedding + self.embed_pos.embedding_weight.to(self.forward_dtype))
+
+        # Scale
+        return self.embed_scale * embedding
+
+    def empty_carry(self, batch_size: int):
+        return TinyRecursiveReasoningModel_ACTV1InnerCarry(
+            z_H=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+            z_L=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+        )
+        
+    def reset_carry(self, reset_flag: torch.Tensor, carry: TinyRecursiveReasoningModel_ACTV1InnerCarry):
+        return TinyRecursiveReasoningModel_ACTV1InnerCarry(
+            z_H=torch.where(reset_flag.view(-1, 1, 1), self.H_init, carry.z_H),
+            z_L=torch.where(reset_flag.view(-1, 1, 1), self.L_init, carry.z_L),
+        )
+
+    def forward(self, carry: TinyRecursiveReasoningModel_ACTV1InnerCarry, batch: Dict[str, torch.Tensor]) -> Tuple[TinyRecursiveReasoningModel_ACTV1InnerCarry, torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        seq_info = dict(
+            cos_sin=self.rotary_emb() if hasattr(self, "rotary_emb") else None,
+        )
+
+        # Input encoding
+        input_embeddings = self._input_embeddings(batch["inputs"], batch["puzzle_identifiers"])
+
+        # Forward iterations
+        it = 0
+        z_H, z_L = carry.z_H, carry.z_L
+        # H_cycles-1 without grad
+        with torch.no_grad():
+            for _H_step in range(self.config.H_cycles-1):
+                for _L_step in range(self.config.L_cycles):
+                    z_L = self.L_level(z_L, z_H + input_embeddings, **seq_info)
+                z_H = self.L_level(z_H, z_L, **seq_info)
+        # 1 with grad
+        for _L_step in range(self.config.L_cycles):
+            z_L = self.L_level(z_L, z_H + input_embeddings, **seq_info)
+        z_H = self.L_level(z_H, z_L, **seq_info)
+
+        # LM Outputs
+        new_carry = TinyRecursiveReasoningModel_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach())  # New carry no grad
+        output = self.lm_head(z_H)[:, self.puzzle_emb_len:]
+        q_logits = self.q_head(z_H[:, 0]).to(torch.float32) # Q-head; uses the first puzzle_emb position
+        return new_carry, output, (q_logits[..., 0], q_logits[..., 1])
+
+
+class TinyRecursiveReasoningModel_ACTV1(nn.Module):
+    """ACT wrapper."""
+
+    def __init__(self, config_dict: dict):
+        super().__init__()
+        self.config = TinyRecursiveReasoningModel_ACTV1Config(**config_dict)
+        self.inner = TinyRecursiveReasoningModel_ACTV1_Inner(self.config)
+
+    @property
+    def puzzle_emb(self):
+        return self.inner.puzzle_emb
+
+    def initial_carry(self, batch: Dict[str, torch.Tensor]):
+        batch_size = batch["inputs"].shape[0]
+
+        return TinyRecursiveReasoningModel_ACTV1Carry(
+            inner_carry=self.inner.empty_carry(batch_size),  # Empty is expected, it will be reseted in first pass as all sequences are halted.
+            
+            steps=torch.zeros((batch_size, ), dtype=torch.int32),
+            halted=torch.ones((batch_size, ), dtype=torch.bool),  # Default to halted
+            
+            current_data={k: torch.empty_like(v) for k, v in batch.items()}
+        )
+        
+    def forward(self, carry: TinyRecursiveReasoningModel_ACTV1Carry, batch: Dict[str, torch.Tensor]) -> Tuple[TinyRecursiveReasoningModel_ACTV1Carry, Dict[str, torch.Tensor]]:
+
+        # Update data, carry (removing halted sequences)
+        new_inner_carry = self.inner.reset_carry(carry.halted, carry.inner_carry)
+        
+        new_steps = torch.where(carry.halted, 0, carry.steps)
+
+        new_current_data = {k: torch.where(carry.halted.view((-1, ) + (1, ) * (batch[k].ndim - 1)), batch[k], v) for k, v in carry.current_data.items()}
+
+        # Forward inner model
+        new_inner_carry, logits, (q_halt_logits, q_continue_logits) = self.inner(new_inner_carry, new_current_data)
+
+        outputs = {
+            "logits": logits,
+            "q_halt_logits": q_halt_logits,
+            "q_continue_logits": q_continue_logits
+        }
+
+        with torch.no_grad():
+            # Step
+            new_steps = new_steps + 1
+            is_last_step = new_steps >= self.config.halt_max_steps
+            
+            halted = is_last_step
+
+            # if training, and ACT is enabled
+            if self.training and (self.config.halt_max_steps > 1):
+
+                # Halt signal
+                # NOTE: During evaluation, always use max steps, this is to guarantee the same halting steps inside a batch for batching purposes
+                
+                if self.config.no_ACT_continue:
+                    halted = halted | (q_halt_logits > 0)
+                else:
+                    halted = halted | (q_halt_logits > q_continue_logits)
+
+                # Exploration
+                min_halt_steps = (torch.rand_like(q_halt_logits) < self.config.halt_exploration_prob) * torch.randint_like(new_steps, low=2, high=self.config.halt_max_steps + 1)
+                halted = halted & (new_steps >= min_halt_steps)
+
+                if not self.config.no_ACT_continue:
+                    # Compute target Q
+                    # NOTE: No replay buffer and target networks for computing target Q-value.
+                    # As batch_size is large, there're many parallel envs.
+                    # Similar concept as PQN https://arxiv.org/abs/2407.04811
+                    _, _, (next_q_halt_logits, next_q_continue_logits), _, _ = self.inner(new_inner_carry, new_current_data)
+                    outputs["target_q_continue"] = torch.sigmoid(torch.where(is_last_step, next_q_halt_logits, torch.maximum(next_q_halt_logits, next_q_continue_logits)))
+
+        return TinyRecursiveReasoningModel_ACTV1Carry(new_inner_carry, new_steps, halted, new_current_data), outputs
\ No newline at end of file
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_mlp_t_sudoku/all_config.yaml b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_mlp_t_sudoku/all_config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2ee94343665548cb39b72c6cf0bed18542060179
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_mlp_t_sudoku/all_config.yaml
@@ -0,0 +1,46 @@
+arch:
+  H_cycles: 3
+  H_layers: 0
+  L_cycles: 6
+  L_layers: 2
+  expansion: 4
+  forward_dtype: bfloat16
+  halt_exploration_prob: 0.1
+  halt_max_steps: 16
+  hidden_size: 512
+  loss:
+    loss_type: stablemax_cross_entropy
+    name: losses@ACTLossHead
+  mlp_t: true
+  name: recursive_reasoning.trm@TinyRecursiveReasoningModel_ACTV1
+  no_ACT_continue: true
+  num_heads: 8
+  pos_encodings: none
+  puzzle_emb_len: 16
+  puzzle_emb_ndim: 512
+beta1: 0.9
+beta2: 0.95
+checkpoint_every_eval: true
+checkpoint_path: checkpoints/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_mlp_t_sudoku
+data_paths:
+- data/sudoku-extreme-1k-aug-1000
+data_paths_test: []
+ema: true
+ema_rate: 0.999
+epochs: 50000
+eval_interval: 5000
+eval_save_outputs: []
+evaluators: []
+freeze_weights: false
+global_batch_size: 768
+load_checkpoint: null
+lr: 0.0001
+lr_min_ratio: 1.0
+lr_warmup_steps: 2000
+min_eval_interval: 0
+project_name: Sudoku-extreme-1k-aug-1000-ACT-torch
+puzzle_emb_lr: 0.0001
+puzzle_emb_weight_decay: 1.0
+run_name: pretrain_mlp_t_sudoku
+seed: 0
+weight_decay: 1.0
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_mlp_t_sudoku/losses.py b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_mlp_t_sudoku/losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..2597cd67cc4530c6536fd1c479a60ce6dea94b79
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_mlp_t_sudoku/losses.py
@@ -0,0 +1,103 @@
+from typing import Any, Tuple, Dict, Sequence, Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+import math
+
+IGNORE_LABEL_ID = -100
+
+
+def s(x, epsilon=1e-30):
+    return torch.where(
+        x<0,
+        1/(1-x+ epsilon),
+        x + 1
+    )
+
+
+def log_stablemax(x, dim=-1):
+    s_x = s(x)
+    return torch.log(s_x/torch.sum(s_x, dim=dim, keepdim=True))
+
+
+def stablemax_cross_entropy(logits, labels, ignore_index: int = -100, valid_mask=None):
+    logprobs = log_stablemax(logits.to(torch.float64), dim=-1)
+
+    if valid_mask is None:
+        valid_mask = (labels != ignore_index)
+    transformed_labels = torch.where(valid_mask, labels, 0)
+    prediction_logprobs = torch.gather(logprobs, index=transformed_labels.to(torch.long).unsqueeze(-1), dim=-1).squeeze(-1)
+
+    return -torch.where(valid_mask, prediction_logprobs, 0)
+
+
+def softmax_cross_entropy(logits, labels, ignore_index: int = -100):
+    # Cast logits to f32
+    # Flatten logits
+    return F.cross_entropy(logits.to(torch.float32).view(-1, logits.shape[-1]), labels.to(torch.long).view(-1), ignore_index=ignore_index, reduction="none").view(labels.shape)
+
+
+class ACTLossHead(nn.Module):
+    def __init__(self, model: nn.Module, loss_type: str):
+        super().__init__()
+        self.model = model
+        self.loss_fn = globals()[loss_type]
+        
+    def initial_carry(self, *args, **kwargs):
+        return self.model.initial_carry(*args, **kwargs)  # type: ignore
+
+    def forward(
+        self,
+        return_keys: Sequence[str],
+        # Model args
+        **model_kwargs,
+    ) -> Tuple[Any, torch.Tensor, Dict[str, torch.Tensor], Optional[Dict[str, torch.Tensor]], torch.Tensor]:
+        # Model logits
+        # B x SeqLen x D
+        new_carry, outputs = self.model(**model_kwargs)
+        labels = new_carry.current_data["labels"]
+
+        with torch.no_grad():
+            # Preds
+            outputs["preds"] = torch.argmax(outputs["logits"], dim=-1)
+
+            # Correctness
+            mask = (labels != IGNORE_LABEL_ID)
+            loss_counts = mask.sum(-1)
+            loss_divisor = loss_counts.clamp_min(1).unsqueeze(-1)  # Avoid NaNs in division
+
+            is_correct = mask & (torch.argmax(outputs["logits"], dim=-1) == labels)
+            seq_is_correct = is_correct.sum(-1) == loss_counts
+            
+            # Metrics (halted)
+            valid_metrics = new_carry.halted & (loss_counts > 0)
+            metrics = {
+                "count": valid_metrics.sum(),
+                
+                "accuracy":       torch.where(valid_metrics, (is_correct.to(torch.float32) / loss_divisor).sum(-1), 0).sum(),
+                "exact_accuracy": (valid_metrics & seq_is_correct).sum(),
+
+                "q_halt_accuracy": (valid_metrics & ((outputs["q_halt_logits"] >= 0) == seq_is_correct)).sum(),
+                "steps":          torch.where(valid_metrics, new_carry.steps, 0).sum(),
+            }
+
+        # Losses
+
+        lm_loss = (self.loss_fn(outputs["logits"], labels, ignore_index=IGNORE_LABEL_ID, valid_mask=mask) / loss_divisor).sum()
+        q_halt_loss = F.binary_cross_entropy_with_logits(outputs["q_halt_logits"], seq_is_correct.to(outputs["q_halt_logits"].dtype), reduction="sum")
+        metrics.update({
+            "lm_loss": lm_loss.detach(),
+            "q_halt_loss": q_halt_loss.detach(),
+        })
+        # Q continue (bootstrapping target loss); Alexia: This fits Q-learning, but seems totally unecessary
+        q_continue_loss = 0
+        if "target_q_continue" in outputs:
+            q_continue_loss = F.binary_cross_entropy_with_logits(outputs["q_continue_logits"], outputs["target_q_continue"], reduction="sum")
+
+            metrics["q_continue_loss"] = q_continue_loss.detach()
+        # Filter outputs for return
+        detached_outputs = {k: outputs[k].detach() for k in return_keys if k in outputs}
+
+        return new_carry, lm_loss + 0.5 * (q_halt_loss + q_continue_loss), metrics, detached_outputs, new_carry.halted.all()
+
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_mlp_t_sudoku/trm.py b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_mlp_t_sudoku/trm.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c3e39dfb6e2f93f0781e9e25af9a12751201482
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_mlp_t_sudoku/trm.py
@@ -0,0 +1,297 @@
+from typing import Tuple, List, Dict, Optional
+from dataclasses import dataclass
+import math
+import torch
+import copy
+import torch.nn.functional as F
+from torch import nn
+from pydantic import BaseModel
+import random
+from models.common import trunc_normal_init_
+from models.layers import rms_norm, LinearSwish, SwiGLU, Attention, RotaryEmbedding, CosSin, CastedEmbedding, CastedLinear
+from models.sparse_embedding import CastedSparseEmbedding
+
+IGNORE_LABEL_ID = -100
+
+@dataclass
+class TinyRecursiveReasoningModel_ACTV1InnerCarry:
+    z_H: torch.Tensor
+    z_L: torch.Tensor
+
+
+@dataclass
+class TinyRecursiveReasoningModel_ACTV1Carry:
+    inner_carry: TinyRecursiveReasoningModel_ACTV1InnerCarry
+    
+    steps: torch.Tensor
+    halted: torch.Tensor
+    
+    current_data: Dict[str, torch.Tensor]
+
+
+class TinyRecursiveReasoningModel_ACTV1Config(BaseModel):
+    batch_size: int
+    seq_len: int
+    puzzle_emb_ndim: int = 0
+    num_puzzle_identifiers: int
+    vocab_size: int
+
+    H_cycles: int
+    L_cycles: int
+
+    H_layers: int # ignored
+    L_layers: int
+
+    # Transformer config
+    hidden_size: int
+    expansion: float
+    num_heads: int
+    pos_encodings: str
+
+    rms_norm_eps: float = 1e-5
+    rope_theta: float = 10000.0
+    
+    # Halting Q-learning config
+    halt_max_steps: int
+    halt_exploration_prob: float
+
+    forward_dtype: str = "bfloat16"
+
+    # Alexia: added
+    mlp_t: bool = False # use mlp on L instead of transformer
+    puzzle_emb_len: int = 16 # if non-zero, its specified to this value
+    no_ACT_continue: bool =  True # No continue ACT loss, only use the sigmoid of the halt which makes much more sense
+
+class TinyRecursiveReasoningModel_ACTV1Block(nn.Module):
+    def __init__(self, config: TinyRecursiveReasoningModel_ACTV1Config) -> None:
+        super().__init__()
+
+        self.config = config
+        if self.config.mlp_t:
+            self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size) if self.config.puzzle_emb_len == 0 else self.config.puzzle_emb_len
+            self.mlp_t = SwiGLU(
+                hidden_size=self.config.seq_len + self.puzzle_emb_len, # L
+                expansion=config.expansion,
+            )
+        else:
+            self.self_attn = Attention(
+                hidden_size=config.hidden_size,
+                head_dim=config.hidden_size // config.num_heads,
+                num_heads=config.num_heads,
+                num_key_value_heads=config.num_heads,
+                causal=False
+            )
+        self.mlp = SwiGLU(
+            hidden_size=config.hidden_size,
+            expansion=config.expansion,
+        )
+        self.norm_eps = config.rms_norm_eps
+
+    def forward(self, cos_sin: CosSin, hidden_states: torch.Tensor) -> torch.Tensor:
+        # B, L, D = hidden_states.shape
+        # Post Norm
+        if self.config.mlp_t:
+            hidden_states = hidden_states.transpose(1,2)
+            out = self.mlp_t(hidden_states)
+            hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+            hidden_states = hidden_states.transpose(1,2)
+        else:
+            # Self Attention
+            hidden_states = rms_norm(hidden_states + self.self_attn(cos_sin=cos_sin, hidden_states=hidden_states), variance_epsilon=self.norm_eps)
+        # Fully Connected
+        out = self.mlp(hidden_states)
+        hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+        return hidden_states
+
+class TinyRecursiveReasoningModel_ACTV1ReasoningModule(nn.Module):
+    def __init__(self, layers: List[TinyRecursiveReasoningModel_ACTV1Block]):
+        super().__init__()
+        self.layers = torch.nn.ModuleList(layers)
+
+    def forward(self, hidden_states: torch.Tensor, input_injection: torch.Tensor, **kwargs) -> torch.Tensor:
+        hidden_states = hidden_states + input_injection
+        for layer in self.layers:
+            hidden_states = layer(hidden_states=hidden_states, **kwargs)
+        return hidden_states
+
+
+class TinyRecursiveReasoningModel_ACTV1_Inner(nn.Module):
+    def __init__(self, config: TinyRecursiveReasoningModel_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        self.forward_dtype = getattr(torch, self.config.forward_dtype)
+
+        # I/O
+
+        self.embed_scale = math.sqrt(self.config.hidden_size)
+        embed_init_std = 1.0 / self.embed_scale
+
+        self.embed_tokens = CastedEmbedding(self.config.vocab_size, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+        self.lm_head      = CastedLinear(self.config.hidden_size, self.config.vocab_size, bias=False)
+        self.q_head       = CastedLinear(self.config.hidden_size, 2, bias=True)
+
+        self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size)  if self.config.puzzle_emb_len == 0 else self.config.puzzle_emb_len  # ceil div
+        if self.config.puzzle_emb_ndim > 0:
+            # Zero init puzzle embeddings
+            self.puzzle_emb = CastedSparseEmbedding(self.config.num_puzzle_identifiers, self.config.puzzle_emb_ndim,
+                                                    batch_size=self.config.batch_size, init_std=0, cast_to=self.forward_dtype)
+
+        # LM Blocks
+        if self.config.pos_encodings == "rope":
+            self.rotary_emb = RotaryEmbedding(dim=self.config.hidden_size // self.config.num_heads,
+                                              max_position_embeddings=self.config.seq_len + self.puzzle_emb_len,
+                                              base=self.config.rope_theta)
+        elif self.config.pos_encodings == "learned":
+            self.embed_pos = CastedEmbedding(self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+        else:
+            pass
+
+        # Reasoning Layers
+        self.L_level = TinyRecursiveReasoningModel_ACTV1ReasoningModule(layers=[TinyRecursiveReasoningModel_ACTV1Block(self.config) for _i in range(self.config.L_layers)])
+
+        # Initial states
+        self.H_init = nn.Buffer(trunc_normal_init_(torch.empty(self.config.hidden_size, dtype=self.forward_dtype), std=1), persistent=True)
+        self.L_init = nn.Buffer(trunc_normal_init_(torch.empty(self.config.hidden_size, dtype=self.forward_dtype), std=1), persistent=True)
+
+        # Q head special init
+        # Init Q to (almost) zero for faster learning during bootstrapping
+        with torch.no_grad():
+            self.q_head.weight.zero_()
+            self.q_head.bias.fill_(-5)  # type: ignore
+
+    def _input_embeddings(self, input: torch.Tensor, puzzle_identifiers: torch.Tensor):
+        # Token embedding
+        embedding = self.embed_tokens(input.to(torch.int32))
+
+        # Puzzle embeddings
+        if self.config.puzzle_emb_ndim > 0:
+            puzzle_embedding = self.puzzle_emb(puzzle_identifiers)
+            
+            pad_count = self.puzzle_emb_len * self.config.hidden_size - puzzle_embedding.shape[-1]
+            if pad_count > 0:
+                puzzle_embedding = F.pad(puzzle_embedding, (0, pad_count))
+
+            embedding = torch.cat((puzzle_embedding.view(-1, self.puzzle_emb_len, self.config.hidden_size), embedding), dim=-2)
+
+        # Position embeddings
+        if self.config.pos_encodings == "learned":
+            # scale by 1/sqrt(2) to maintain forward variance
+            embedding = 0.707106781 * (embedding + self.embed_pos.embedding_weight.to(self.forward_dtype))
+
+        # Scale
+        return self.embed_scale * embedding
+
+    def empty_carry(self, batch_size: int):
+        return TinyRecursiveReasoningModel_ACTV1InnerCarry(
+            z_H=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+            z_L=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+        )
+        
+    def reset_carry(self, reset_flag: torch.Tensor, carry: TinyRecursiveReasoningModel_ACTV1InnerCarry):
+        return TinyRecursiveReasoningModel_ACTV1InnerCarry(
+            z_H=torch.where(reset_flag.view(-1, 1, 1), self.H_init, carry.z_H),
+            z_L=torch.where(reset_flag.view(-1, 1, 1), self.L_init, carry.z_L),
+        )
+
+    def forward(self, carry: TinyRecursiveReasoningModel_ACTV1InnerCarry, batch: Dict[str, torch.Tensor]) -> Tuple[TinyRecursiveReasoningModel_ACTV1InnerCarry, torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        seq_info = dict(
+            cos_sin=self.rotary_emb() if hasattr(self, "rotary_emb") else None,
+        )
+
+        # Input encoding
+        input_embeddings = self._input_embeddings(batch["inputs"], batch["puzzle_identifiers"])
+
+        # Forward iterations
+        it = 0
+        z_H, z_L = carry.z_H, carry.z_L
+        # H_cycles-1 without grad
+        with torch.no_grad():
+            for _H_step in range(self.config.H_cycles-1):
+                for _L_step in range(self.config.L_cycles):
+                    z_L = self.L_level(z_L, z_H + input_embeddings, **seq_info)
+                z_H = self.L_level(z_H, z_L, **seq_info)
+        # 1 with grad
+        for _L_step in range(self.config.L_cycles):
+            z_L = self.L_level(z_L, z_H + input_embeddings, **seq_info)
+        z_H = self.L_level(z_H, z_L, **seq_info)
+
+        # LM Outputs
+        new_carry = TinyRecursiveReasoningModel_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach())  # New carry no grad
+        output = self.lm_head(z_H)[:, self.puzzle_emb_len:]
+        q_logits = self.q_head(z_H[:, 0]).to(torch.float32) # Q-head; uses the first puzzle_emb position
+        return new_carry, output, (q_logits[..., 0], q_logits[..., 1])
+
+
+class TinyRecursiveReasoningModel_ACTV1(nn.Module):
+    """ACT wrapper."""
+
+    def __init__(self, config_dict: dict):
+        super().__init__()
+        self.config = TinyRecursiveReasoningModel_ACTV1Config(**config_dict)
+        self.inner = TinyRecursiveReasoningModel_ACTV1_Inner(self.config)
+
+    @property
+    def puzzle_emb(self):
+        return self.inner.puzzle_emb
+
+    def initial_carry(self, batch: Dict[str, torch.Tensor]):
+        batch_size = batch["inputs"].shape[0]
+
+        return TinyRecursiveReasoningModel_ACTV1Carry(
+            inner_carry=self.inner.empty_carry(batch_size),  # Empty is expected, it will be reseted in first pass as all sequences are halted.
+            
+            steps=torch.zeros((batch_size, ), dtype=torch.int32),
+            halted=torch.ones((batch_size, ), dtype=torch.bool),  # Default to halted
+            
+            current_data={k: torch.empty_like(v) for k, v in batch.items()}
+        )
+        
+    def forward(self, carry: TinyRecursiveReasoningModel_ACTV1Carry, batch: Dict[str, torch.Tensor]) -> Tuple[TinyRecursiveReasoningModel_ACTV1Carry, Dict[str, torch.Tensor]]:
+
+        # Update data, carry (removing halted sequences)
+        new_inner_carry = self.inner.reset_carry(carry.halted, carry.inner_carry)
+        
+        new_steps = torch.where(carry.halted, 0, carry.steps)
+
+        new_current_data = {k: torch.where(carry.halted.view((-1, ) + (1, ) * (batch[k].ndim - 1)), batch[k], v) for k, v in carry.current_data.items()}
+
+        # Forward inner model
+        new_inner_carry, logits, (q_halt_logits, q_continue_logits) = self.inner(new_inner_carry, new_current_data)
+
+        outputs = {
+            "logits": logits,
+            "q_halt_logits": q_halt_logits,
+            "q_continue_logits": q_continue_logits
+        }
+
+        with torch.no_grad():
+            # Step
+            new_steps = new_steps + 1
+            is_last_step = new_steps >= self.config.halt_max_steps
+            
+            halted = is_last_step
+
+            # if training, and ACT is enabled
+            if self.training and (self.config.halt_max_steps > 1):
+
+                # Halt signal
+                # NOTE: During evaluation, always use max steps, this is to guarantee the same halting steps inside a batch for batching purposes
+                
+                if self.config.no_ACT_continue:
+                    halted = halted | (q_halt_logits > 0)
+                else:
+                    halted = halted | (q_halt_logits > q_continue_logits)
+
+                # Exploration
+                min_halt_steps = (torch.rand_like(q_halt_logits) < self.config.halt_exploration_prob) * torch.randint_like(new_steps, low=2, high=self.config.halt_max_steps + 1)
+                halted = halted & (new_steps >= min_halt_steps)
+
+                if not self.config.no_ACT_continue:
+                    # Compute target Q
+                    # NOTE: No replay buffer and target networks for computing target Q-value.
+                    # As batch_size is large, there're many parallel envs.
+                    # Similar concept as PQN https://arxiv.org/abs/2407.04811
+                    _, _, (next_q_halt_logits, next_q_continue_logits), _, _ = self.inner(new_inner_carry, new_current_data)
+                    outputs["target_q_continue"] = torch.sigmoid(torch.where(is_last_step, next_q_halt_logits, torch.maximum(next_q_halt_logits, next_q_continue_logits)))
+
+        return TinyRecursiveReasoningModel_ACTV1Carry(new_inner_carry, new_steps, halted, new_current_data), outputs
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_mlpt_mamba_sudoku/all_config.yaml b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_mlpt_mamba_sudoku/all_config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..6015ba2c2b15a532d3b4fb1e71c767bd9f7773d9
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_mlpt_mamba_sudoku/all_config.yaml
@@ -0,0 +1,53 @@
+arch:
+  H_cycles: 3
+  H_layers: 0
+  L_cycles: 6
+  L_layers: 2
+  expansion: 4
+  ffn_type: mamba
+  forward_dtype: bfloat16
+  halt_exploration_prob: 0.1
+  halt_max_steps: 16
+  hidden_size: 512
+  loss:
+    loss_type: stablemax_cross_entropy
+    name: losses@ACTLossHead
+  mamba_bias: false
+  mamba_d_conv: 4
+  mamba_d_state: 16
+  mamba_dt_rank: auto
+  mamba_expand: 2
+  mixer: mlp_t
+  mlp_t: false
+  name: recursive_reasoning.trm@TinyRecursiveReasoningModel_ACTV1
+  no_ACT_continue: true
+  num_heads: 8
+  pos_encodings: none
+  puzzle_emb_len: 16
+  puzzle_emb_ndim: 512
+beta1: 0.9
+beta2: 0.95
+checkpoint_every_eval: true
+checkpoint_path: checkpoints/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_mlpt_mamba_sudoku
+data_paths:
+- data/sudoku-extreme-1k-aug-1000
+data_paths_test: []
+ema: true
+ema_rate: 0.999
+epochs: 50000
+eval_interval: 5000
+eval_save_outputs: []
+evaluators: []
+freeze_weights: false
+global_batch_size: 768
+load_checkpoint: null
+lr: 0.0001
+lr_min_ratio: 1.0
+lr_warmup_steps: 2000
+min_eval_interval: 0
+project_name: Sudoku-extreme-1k-aug-1000-ACT-torch
+puzzle_emb_lr: 0.0001
+puzzle_emb_weight_decay: 1.0
+run_name: pretrain_mlpt_mamba_sudoku
+seed: 0
+weight_decay: 1.0
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_mlpt_mamba_sudoku/losses.py b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_mlpt_mamba_sudoku/losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..2597cd67cc4530c6536fd1c479a60ce6dea94b79
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_mlpt_mamba_sudoku/losses.py
@@ -0,0 +1,103 @@
+from typing import Any, Tuple, Dict, Sequence, Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+import math
+
+IGNORE_LABEL_ID = -100
+
+
+def s(x, epsilon=1e-30):
+    return torch.where(
+        x<0,
+        1/(1-x+ epsilon),
+        x + 1
+    )
+
+
+def log_stablemax(x, dim=-1):
+    s_x = s(x)
+    return torch.log(s_x/torch.sum(s_x, dim=dim, keepdim=True))
+
+
+def stablemax_cross_entropy(logits, labels, ignore_index: int = -100, valid_mask=None):
+    logprobs = log_stablemax(logits.to(torch.float64), dim=-1)
+
+    if valid_mask is None:
+        valid_mask = (labels != ignore_index)
+    transformed_labels = torch.where(valid_mask, labels, 0)
+    prediction_logprobs = torch.gather(logprobs, index=transformed_labels.to(torch.long).unsqueeze(-1), dim=-1).squeeze(-1)
+
+    return -torch.where(valid_mask, prediction_logprobs, 0)
+
+
+def softmax_cross_entropy(logits, labels, ignore_index: int = -100):
+    # Cast logits to f32
+    # Flatten logits
+    return F.cross_entropy(logits.to(torch.float32).view(-1, logits.shape[-1]), labels.to(torch.long).view(-1), ignore_index=ignore_index, reduction="none").view(labels.shape)
+
+
+class ACTLossHead(nn.Module):
+    def __init__(self, model: nn.Module, loss_type: str):
+        super().__init__()
+        self.model = model
+        self.loss_fn = globals()[loss_type]
+        
+    def initial_carry(self, *args, **kwargs):
+        return self.model.initial_carry(*args, **kwargs)  # type: ignore
+
+    def forward(
+        self,
+        return_keys: Sequence[str],
+        # Model args
+        **model_kwargs,
+    ) -> Tuple[Any, torch.Tensor, Dict[str, torch.Tensor], Optional[Dict[str, torch.Tensor]], torch.Tensor]:
+        # Model logits
+        # B x SeqLen x D
+        new_carry, outputs = self.model(**model_kwargs)
+        labels = new_carry.current_data["labels"]
+
+        with torch.no_grad():
+            # Preds
+            outputs["preds"] = torch.argmax(outputs["logits"], dim=-1)
+
+            # Correctness
+            mask = (labels != IGNORE_LABEL_ID)
+            loss_counts = mask.sum(-1)
+            loss_divisor = loss_counts.clamp_min(1).unsqueeze(-1)  # Avoid NaNs in division
+
+            is_correct = mask & (torch.argmax(outputs["logits"], dim=-1) == labels)
+            seq_is_correct = is_correct.sum(-1) == loss_counts
+            
+            # Metrics (halted)
+            valid_metrics = new_carry.halted & (loss_counts > 0)
+            metrics = {
+                "count": valid_metrics.sum(),
+                
+                "accuracy":       torch.where(valid_metrics, (is_correct.to(torch.float32) / loss_divisor).sum(-1), 0).sum(),
+                "exact_accuracy": (valid_metrics & seq_is_correct).sum(),
+
+                "q_halt_accuracy": (valid_metrics & ((outputs["q_halt_logits"] >= 0) == seq_is_correct)).sum(),
+                "steps":          torch.where(valid_metrics, new_carry.steps, 0).sum(),
+            }
+
+        # Losses
+
+        lm_loss = (self.loss_fn(outputs["logits"], labels, ignore_index=IGNORE_LABEL_ID, valid_mask=mask) / loss_divisor).sum()
+        q_halt_loss = F.binary_cross_entropy_with_logits(outputs["q_halt_logits"], seq_is_correct.to(outputs["q_halt_logits"].dtype), reduction="sum")
+        metrics.update({
+            "lm_loss": lm_loss.detach(),
+            "q_halt_loss": q_halt_loss.detach(),
+        })
+        # Q continue (bootstrapping target loss); Alexia: This fits Q-learning, but seems totally unecessary
+        q_continue_loss = 0
+        if "target_q_continue" in outputs:
+            q_continue_loss = F.binary_cross_entropy_with_logits(outputs["q_continue_logits"], outputs["target_q_continue"], reduction="sum")
+
+            metrics["q_continue_loss"] = q_continue_loss.detach()
+        # Filter outputs for return
+        detached_outputs = {k: outputs[k].detach() for k in return_keys if k in outputs}
+
+        return new_carry, lm_loss + 0.5 * (q_halt_loss + q_continue_loss), metrics, detached_outputs, new_carry.halted.all()
+
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_mlpt_mamba_sudoku/trm.py b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_mlpt_mamba_sudoku/trm.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d62fff4afe16947d9fce9693fbd77246b75fa9e
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_mlpt_mamba_sudoku/trm.py
@@ -0,0 +1,363 @@
+from typing import Tuple, List, Dict, Optional, Union
+from dataclasses import dataclass
+import math
+import torch
+import copy
+import torch.nn.functional as F
+from torch import nn
+from pydantic import BaseModel
+import random
+from models.common import trunc_normal_init_
+from models.layers import rms_norm, LinearSwish, SwiGLU, Attention, RotaryEmbedding, CosSin, CastedEmbedding, CastedLinear
+from models.sparse_embedding import CastedSparseEmbedding
+
+IGNORE_LABEL_ID = -100
+
+@dataclass
+class TinyRecursiveReasoningModel_ACTV1InnerCarry:
+    z_H: torch.Tensor
+    z_L: torch.Tensor
+
+
+@dataclass
+class TinyRecursiveReasoningModel_ACTV1Carry:
+    inner_carry: TinyRecursiveReasoningModel_ACTV1InnerCarry
+    
+    steps: torch.Tensor
+    halted: torch.Tensor
+    
+    current_data: Dict[str, torch.Tensor]
+
+
+class TinyRecursiveReasoningModel_ACTV1Config(BaseModel):
+    batch_size: int
+    seq_len: int
+    puzzle_emb_ndim: int = 0
+    num_puzzle_identifiers: int
+    vocab_size: int
+
+    H_cycles: int
+    L_cycles: int
+
+    H_layers: int # ignored
+    L_layers: int
+
+    # Transformer config
+    hidden_size: int
+    expansion: float
+    num_heads: int
+    pos_encodings: str
+
+    rms_norm_eps: float = 1e-5
+    rope_theta: float = 10000.0
+    
+    # Halting Q-learning config
+    halt_max_steps: int
+    halt_exploration_prob: float
+
+    forward_dtype: str = "bfloat16"
+
+    # Alexia: added
+    mlp_t: bool = False # use mlp on L instead of transformer
+    puzzle_emb_len: int = 16 # if non-zero, its specified to this value
+    no_ACT_continue: bool =  True # No continue ACT loss, only use the sigmoid of the halt which makes much more sense
+    
+    # Mixer/FFN selection (backward-compatible defaults)
+    mixer: str = "attention"  # options: attention | mlp_t | mamba
+    ffn_type: str = "mlp"     # options: mlp | mamba
+
+    # Mamba hyperparameters
+    mamba_expand: int = 2
+    mamba_d_state: int = 16
+    mamba_d_conv: int = 4
+    mamba_dt_rank: Union[int, str] = "auto"
+    mamba_bias: bool = False
+
+class TinyRecursiveReasoningModel_ACTV1Block(nn.Module):
+    def __init__(self, config: TinyRecursiveReasoningModel_ACTV1Config) -> None:
+        super().__init__()
+
+        self.config = config
+        mixer_mode = getattr(self.config, "mixer", "attention")
+        # Backward-compat: honor legacy mlp_t flag if mixer not explicitly set
+        if mixer_mode == "attention" and getattr(self.config, "mlp_t", False):
+            mixer_mode = "mlp_t"
+
+        if mixer_mode == "mlp_t":
+            self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size) if self.config.puzzle_emb_len == 0 else self.config.puzzle_emb_len
+            self.mlp_t = SwiGLU(
+                hidden_size=self.config.seq_len + self.puzzle_emb_len, # L
+                expansion=config.expansion,
+            )
+        elif mixer_mode == "attention":
+            self.self_attn = Attention(
+                hidden_size=config.hidden_size,
+                head_dim=config.hidden_size // config.num_heads,
+                num_heads=config.num_heads,
+                num_key_value_heads=config.num_heads,
+                causal=False
+            )
+        elif mixer_mode == "mamba":
+            try:
+                from mamba_ssm.modules.mamba_simple import Mamba
+            except Exception as e:
+                raise ImportError("mamba_ssm is not available. Please install with 'pip install -v ./mamba' from repo root.") from e
+            # Resolve dtype from config (e.g., "bfloat16") to torch dtype
+            m_dtype = getattr(torch, self.config.forward_dtype, torch.float32)
+            self.mamba_mixer = Mamba(
+                d_model=config.hidden_size,
+                expand=self.config.mamba_expand,
+                d_state=self.config.mamba_d_state,
+                d_conv=self.config.mamba_d_conv,
+                dt_rank=self.config.mamba_dt_rank,
+                bias=self.config.mamba_bias,
+                dtype=m_dtype,
+            )
+        else:
+            raise ValueError(f"Unsupported mixer: {mixer_mode}")
+
+        # FFN selection
+        if getattr(self.config, "ffn_type", "mlp") == "mamba":
+            try:
+                from mamba_ssm.modules.mamba_simple import Mamba
+            except Exception as e:
+                raise ImportError("mamba_ssm is not available. Please install with 'pip install -v ./mamba' from repo root.") from e
+            # Resolve dtype from config (e.g., "bfloat16") to torch dtype
+            ffn_dtype = getattr(torch, self.config.forward_dtype, torch.float32)
+            self.ffn = Mamba(
+                d_model=config.hidden_size,
+                expand=self.config.mamba_expand,
+                d_state=self.config.mamba_d_state,
+                d_conv=self.config.mamba_d_conv,
+                dt_rank=self.config.mamba_dt_rank,
+                bias=self.config.mamba_bias,
+                dtype=ffn_dtype,
+            )
+        else:
+            self.ffn = SwiGLU(
+                hidden_size=config.hidden_size,
+                expansion=config.expansion,
+            )
+        self.norm_eps = config.rms_norm_eps
+
+    def forward(self, cos_sin: CosSin, hidden_states: torch.Tensor) -> torch.Tensor:
+        # B, L, D = hidden_states.shape
+        # Post Norm
+        mixer_mode = getattr(self.config, "mixer", "attention")
+        if mixer_mode == "attention" and getattr(self.config, "mlp_t", False):
+            mixer_mode = "mlp_t"
+
+        if mixer_mode == "mlp_t":
+            hidden_states = hidden_states.transpose(1,2)
+            out = self.mlp_t(hidden_states)
+            hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+            hidden_states = hidden_states.transpose(1,2)
+        elif mixer_mode == "attention":
+            hidden_states = rms_norm(hidden_states + self.self_attn(cos_sin=cos_sin, hidden_states=hidden_states), variance_epsilon=self.norm_eps)
+        elif mixer_mode == "mamba":
+            # Run Mamba in the model's forward dtype directly (bf16 expected)
+            out = getattr(self, "mamba_mixer")(hidden_states)
+            hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+        else:
+            raise ValueError(f"Unsupported mixer: {mixer_mode}")
+
+        # FFN
+        if getattr(self.config, "ffn_type", "mlp") == "mamba":
+            out = self.ffn(hidden_states)
+        else:
+            out = self.ffn(hidden_states)
+        hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+        return hidden_states
+
+class TinyRecursiveReasoningModel_ACTV1ReasoningModule(nn.Module):
+    def __init__(self, layers: List[TinyRecursiveReasoningModel_ACTV1Block]):
+        super().__init__()
+        self.layers = torch.nn.ModuleList(layers)
+
+    def forward(self, hidden_states: torch.Tensor, input_injection: torch.Tensor, **kwargs) -> torch.Tensor:
+        hidden_states = hidden_states + input_injection
+        for layer in self.layers:
+            hidden_states = layer(hidden_states=hidden_states, **kwargs)
+        return hidden_states
+
+
+class TinyRecursiveReasoningModel_ACTV1_Inner(nn.Module):
+    def __init__(self, config: TinyRecursiveReasoningModel_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        self.forward_dtype = getattr(torch, self.config.forward_dtype)
+
+        # I/O
+
+        self.embed_scale = math.sqrt(self.config.hidden_size)
+        embed_init_std = 1.0 / self.embed_scale
+
+        self.embed_tokens = CastedEmbedding(self.config.vocab_size, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+        self.lm_head      = CastedLinear(self.config.hidden_size, self.config.vocab_size, bias=False)
+        self.q_head       = CastedLinear(self.config.hidden_size, 2, bias=True)
+
+        self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size)  if self.config.puzzle_emb_len == 0 else self.config.puzzle_emb_len  # ceil div
+        if self.config.puzzle_emb_ndim > 0:
+            # Zero init puzzle embeddings
+            self.puzzle_emb = CastedSparseEmbedding(self.config.num_puzzle_identifiers, self.config.puzzle_emb_ndim,
+                                                    batch_size=self.config.batch_size, init_std=0, cast_to=self.forward_dtype)
+
+        # LM Blocks
+        if (self.config.pos_encodings == "rope") and (getattr(self.config, "mixer", "attention") == "attention"):
+            self.rotary_emb = RotaryEmbedding(dim=self.config.hidden_size // self.config.num_heads,
+                                              max_position_embeddings=self.config.seq_len + self.puzzle_emb_len,
+                                              base=self.config.rope_theta)
+        elif self.config.pos_encodings == "learned":
+            self.embed_pos = CastedEmbedding(self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+        else:
+            pass
+
+        # Reasoning Layers
+        self.L_level = TinyRecursiveReasoningModel_ACTV1ReasoningModule(layers=[TinyRecursiveReasoningModel_ACTV1Block(self.config) for _i in range(self.config.L_layers)])
+
+        # Initial states
+        self.H_init = nn.Buffer(trunc_normal_init_(torch.empty(self.config.hidden_size, dtype=self.forward_dtype), std=1), persistent=True)
+        self.L_init = nn.Buffer(trunc_normal_init_(torch.empty(self.config.hidden_size, dtype=self.forward_dtype), std=1), persistent=True)
+
+        # Q head special init
+        # Init Q to (almost) zero for faster learning during bootstrapping
+        with torch.no_grad():
+            self.q_head.weight.zero_()
+            self.q_head.bias.fill_(-5)  # type: ignore
+
+    def _input_embeddings(self, input: torch.Tensor, puzzle_identifiers: torch.Tensor):
+        # Token embedding
+        embedding = self.embed_tokens(input.to(torch.int32))
+
+        # Puzzle embeddings
+        if self.config.puzzle_emb_ndim > 0:
+            puzzle_embedding = self.puzzle_emb(puzzle_identifiers)
+            
+            pad_count = self.puzzle_emb_len * self.config.hidden_size - puzzle_embedding.shape[-1]
+            if pad_count > 0:
+                puzzle_embedding = F.pad(puzzle_embedding, (0, pad_count))
+
+            embedding = torch.cat((puzzle_embedding.view(-1, self.puzzle_emb_len, self.config.hidden_size), embedding), dim=-2)
+
+        # Position embeddings
+        if self.config.pos_encodings == "learned":
+            # scale by 1/sqrt(2) to maintain forward variance
+            embedding = 0.707106781 * (embedding + self.embed_pos.embedding_weight.to(self.forward_dtype))
+
+        # Scale
+        return self.embed_scale * embedding
+
+    def empty_carry(self, batch_size: int):
+        return TinyRecursiveReasoningModel_ACTV1InnerCarry(
+            z_H=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+            z_L=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+        )
+        
+    def reset_carry(self, reset_flag: torch.Tensor, carry: TinyRecursiveReasoningModel_ACTV1InnerCarry):
+        return TinyRecursiveReasoningModel_ACTV1InnerCarry(
+            z_H=torch.where(reset_flag.view(-1, 1, 1), self.H_init, carry.z_H),
+            z_L=torch.where(reset_flag.view(-1, 1, 1), self.L_init, carry.z_L),
+        )
+
+    def forward(self, carry: TinyRecursiveReasoningModel_ACTV1InnerCarry, batch: Dict[str, torch.Tensor]) -> Tuple[TinyRecursiveReasoningModel_ACTV1InnerCarry, torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        seq_info = dict(
+            cos_sin=self.rotary_emb() if hasattr(self, "rotary_emb") else None,
+        )
+
+        # Input encoding
+        input_embeddings = self._input_embeddings(batch["inputs"], batch["puzzle_identifiers"])
+
+        # Forward iterations
+        it = 0
+        z_H, z_L = carry.z_H, carry.z_L
+        # H_cycles-1 without grad
+        with torch.no_grad():
+            for _H_step in range(self.config.H_cycles-1):
+                for _L_step in range(self.config.L_cycles):
+                    z_L = self.L_level(z_L, z_H + input_embeddings, **seq_info)
+                z_H = self.L_level(z_H, z_L, **seq_info)
+        # 1 with grad
+        for _L_step in range(self.config.L_cycles):
+            z_L = self.L_level(z_L, z_H + input_embeddings, **seq_info)
+        z_H = self.L_level(z_H, z_L, **seq_info)
+
+        # LM Outputs
+        new_carry = TinyRecursiveReasoningModel_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach())  # New carry no grad
+        output = self.lm_head(z_H)[:, self.puzzle_emb_len:]
+        q_logits = self.q_head(z_H[:, 0]).to(torch.float32) # Q-head; uses the first puzzle_emb position
+        return new_carry, output, (q_logits[..., 0], q_logits[..., 1])
+
+
+class TinyRecursiveReasoningModel_ACTV1(nn.Module):
+    """ACT wrapper."""
+
+    def __init__(self, config_dict: dict):
+        super().__init__()
+        self.config = TinyRecursiveReasoningModel_ACTV1Config(**config_dict)
+        self.inner = TinyRecursiveReasoningModel_ACTV1_Inner(self.config)
+
+    @property
+    def puzzle_emb(self):
+        return self.inner.puzzle_emb
+
+    def initial_carry(self, batch: Dict[str, torch.Tensor]):
+        batch_size = batch["inputs"].shape[0]
+
+        return TinyRecursiveReasoningModel_ACTV1Carry(
+            inner_carry=self.inner.empty_carry(batch_size),  # Empty is expected, it will be reseted in first pass as all sequences are halted.
+            
+            steps=torch.zeros((batch_size, ), dtype=torch.int32),
+            halted=torch.ones((batch_size, ), dtype=torch.bool),  # Default to halted
+            
+            current_data={k: torch.empty_like(v) for k, v in batch.items()}
+        )
+        
+    def forward(self, carry: TinyRecursiveReasoningModel_ACTV1Carry, batch: Dict[str, torch.Tensor]) -> Tuple[TinyRecursiveReasoningModel_ACTV1Carry, Dict[str, torch.Tensor]]:
+
+        # Update data, carry (removing halted sequences)
+        new_inner_carry = self.inner.reset_carry(carry.halted, carry.inner_carry)
+        
+        new_steps = torch.where(carry.halted, 0, carry.steps)
+
+        new_current_data = {k: torch.where(carry.halted.view((-1, ) + (1, ) * (batch[k].ndim - 1)), batch[k], v) for k, v in carry.current_data.items()}
+
+        # Forward inner model
+        new_inner_carry, logits, (q_halt_logits, q_continue_logits) = self.inner(new_inner_carry, new_current_data)
+
+        outputs = {
+            "logits": logits,
+            "q_halt_logits": q_halt_logits,
+            "q_continue_logits": q_continue_logits
+        }
+
+        with torch.no_grad():
+            # Step
+            new_steps = new_steps + 1
+            is_last_step = new_steps >= self.config.halt_max_steps
+            
+            halted = is_last_step
+
+            # if training, and ACT is enabled
+            if self.training and (self.config.halt_max_steps > 1):
+
+                # Halt signal
+                # NOTE: During evaluation, always use max steps, this is to guarantee the same halting steps inside a batch for batching purposes
+                
+                if self.config.no_ACT_continue:
+                    halted = halted | (q_halt_logits > 0)
+                else:
+                    halted = halted | (q_halt_logits > q_continue_logits)
+
+                # Exploration
+                min_halt_steps = (torch.rand_like(q_halt_logits) < self.config.halt_exploration_prob) * torch.randint_like(new_steps, low=2, high=self.config.halt_max_steps + 1)
+                halted = halted & (new_steps >= min_halt_steps)
+
+                if not self.config.no_ACT_continue:
+                    # Compute target Q
+                    # NOTE: No replay buffer and target networks for computing target Q-value.
+                    # As batch_size is large, there're many parallel envs.
+                    # Similar concept as PQN https://arxiv.org/abs/2407.04811
+                    _, _, (next_q_halt_logits, next_q_continue_logits), _, _ = self.inner(new_inner_carry, new_current_data)
+                    outputs["target_q_continue"] = torch.sigmoid(torch.where(is_last_step, next_q_halt_logits, torch.maximum(next_q_halt_logits, next_q_continue_logits)))
+
+        return TinyRecursiveReasoningModel_ACTV1Carry(new_inner_carry, new_steps, halted, new_current_data), outputs
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_rwkv_sudoku/all_config.yaml b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_rwkv_sudoku/all_config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3de3e89396f36c08bdeea1fd9c5c0317ab4c4ef7
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_rwkv_sudoku/all_config.yaml
@@ -0,0 +1,48 @@
+arch:
+  H_cycles: 3
+  H_layers: 0
+  L_cycles: 6
+  L_layers: 2
+  expansion: 4
+  forward_dtype: bfloat16
+  halt_exploration_prob: 0.1
+  halt_max_steps: 16
+  hidden_size: 512
+  loss:
+    loss_type: stablemax_cross_entropy
+    name: losses@ACTLossHead
+  mlp_t: false
+  name: recursive_reasoning.trm@TinyRecursiveReasoningModel_ACTV1
+  no_ACT_continue: true
+  num_heads: 8
+  pos_encodings: rope
+  puzzle_emb_len: 16
+  puzzle_emb_ndim: 512
+  rwkv_head_size: 64
+  use_rwkv: true
+beta1: 0.9
+beta2: 0.95
+checkpoint_every_eval: true
+checkpoint_path: checkpoints/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_rwkv_sudoku
+data_paths:
+- data/sudoku-extreme-1k-aug-1000
+data_paths_test: []
+ema: true
+ema_rate: 0.999
+epochs: 50000
+eval_interval: 5000
+eval_save_outputs: []
+evaluators: []
+freeze_weights: false
+global_batch_size: 768
+load_checkpoint: null
+lr: 0.0001
+lr_min_ratio: 1.0
+lr_warmup_steps: 2000
+min_eval_interval: 0
+project_name: Sudoku-extreme-1k-aug-1000-ACT-torch
+puzzle_emb_lr: 0.0001
+puzzle_emb_weight_decay: 1.0
+run_name: pretrain_rwkv_sudoku
+seed: 0
+weight_decay: 1.0
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_rwkv_sudoku/losses.py b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_rwkv_sudoku/losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..cdfb77718bc16f98cdd7da06eb5795c36b866aa7
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_rwkv_sudoku/losses.py
@@ -0,0 +1,102 @@
+from typing import Any, Tuple, Dict, Sequence, Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+import math
+
+IGNORE_LABEL_ID = -100
+
+
+def s(x, epsilon=1e-30):
+    return torch.where(
+        x<0,
+        1/(1-x+ epsilon),
+        x + 1
+    )
+
+
+def log_stablemax(x, dim=-1):
+    s_x = s(x)
+    return torch.log(s_x/torch.sum(s_x, dim=dim, keepdim=True))
+
+
+def stablemax_cross_entropy(logits, labels, ignore_index: int = -100, valid_mask=None):
+    logprobs = log_stablemax(logits.to(torch.float64), dim=-1)
+
+    if valid_mask is None:
+        valid_mask = (labels != ignore_index)
+    transformed_labels = torch.where(valid_mask, labels, 0)
+    prediction_logprobs = torch.gather(logprobs, index=transformed_labels.to(torch.long).unsqueeze(-1), dim=-1).squeeze(-1)
+
+    return -torch.where(valid_mask, prediction_logprobs, 0)
+
+
+def softmax_cross_entropy(logits, labels, ignore_index: int = -100):
+    # Cast logits to f32
+    # Flatten logits
+    return F.cross_entropy(logits.to(torch.float32).view(-1, logits.shape[-1]), labels.to(torch.long).view(-1), ignore_index=ignore_index, reduction="none").view(labels.shape)
+
+
+class ACTLossHead(nn.Module):
+    def __init__(self, model: nn.Module, loss_type: str):
+        super().__init__()
+        self.model = model
+        self.loss_fn = globals()[loss_type]
+        
+    def initial_carry(self, *args, **kwargs):
+        return self.model.initial_carry(*args, **kwargs)  # type: ignore
+
+    def forward(
+        self,
+        return_keys: Sequence[str],
+        # Model args
+        **model_kwargs,
+    ) -> Tuple[Any, torch.Tensor, Dict[str, torch.Tensor], Optional[Dict[str, torch.Tensor]], torch.Tensor]:
+        # Model logits
+        # B x SeqLen x D
+        new_carry, outputs = self.model(**model_kwargs)
+        labels = new_carry.current_data["labels"]
+
+        with torch.no_grad():
+            # Preds
+            outputs["preds"] = torch.argmax(outputs["logits"], dim=-1)
+
+            # Correctness
+            mask = (labels != IGNORE_LABEL_ID)
+            loss_counts = mask.sum(-1)
+            loss_divisor = loss_counts.clamp_min(1).unsqueeze(-1)  # Avoid NaNs in division
+
+            is_correct = mask & (torch.argmax(outputs["logits"], dim=-1) == labels)
+            seq_is_correct = is_correct.sum(-1) == loss_counts
+            
+            # Metrics (halted)
+            valid_metrics = new_carry.halted & (loss_counts > 0)
+            metrics = {
+                "count": valid_metrics.sum(),
+                
+                "accuracy":       torch.where(valid_metrics, (is_correct.to(torch.float32) / loss_divisor).sum(-1), 0).sum(),
+                "exact_accuracy": (valid_metrics & seq_is_correct).sum(),
+
+                "q_halt_accuracy": (valid_metrics & ((outputs["q_halt_logits"] >= 0) == seq_is_correct)).sum(),
+                "steps":          torch.where(valid_metrics, new_carry.steps, 0).sum(),
+            }
+
+        # Losses
+
+        lm_loss = (self.loss_fn(outputs["logits"], labels, ignore_index=IGNORE_LABEL_ID, valid_mask=mask) / loss_divisor).sum()
+        q_halt_loss = F.binary_cross_entropy_with_logits(outputs["q_halt_logits"], seq_is_correct.to(outputs["q_halt_logits"].dtype), reduction="sum")
+        metrics.update({
+            "lm_loss": lm_loss.detach(),
+            "q_halt_loss": q_halt_loss.detach(),
+        })
+        # Q continue (bootstrapping target loss); Alexia: This fits Q-learning, but seems totally unecessary
+        q_continue_loss = 0
+        if "target_q_continue" in outputs:
+            q_continue_loss = F.binary_cross_entropy_with_logits(outputs["q_continue_logits"], outputs["target_q_continue"], reduction="sum")
+
+            metrics["q_continue_loss"] = q_continue_loss.detach()
+        # Filter outputs for return
+        detached_outputs = {k: outputs[k].detach() for k in return_keys if k in outputs}
+
+        return new_carry, lm_loss + 0.5 * (q_halt_loss + q_continue_loss), metrics, detached_outputs, new_carry.halted.all()
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_rwkv_sudoku/trm.py b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_rwkv_sudoku/trm.py
new file mode 100644
index 0000000000000000000000000000000000000000..e0e8dde8fac6f2cb37ad77e0e5803c5912b2bc2f
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_rwkv_sudoku/trm.py
@@ -0,0 +1,601 @@
+# === Tiny Recursive Reasoning Model (TRM) with optional RWKV mixer ==========================
+# - Keeps TRM's commit-last / backprop-last semantics.
+# - Adds RWKV-v7 TimeMix + ChannelMix as an alternative L-level mixer.
+# - Defaults remain identical to your original file unless cfg.use_rwkv=True.
+#
+# RWKV parts adapted from: RWKV-LM/RWKV-v7/rwkv_v7_demo.py (TimeMix & ChannelMix).  [ref]
+# TRM base taken from your TinyRecursiveReasoningModel_ACTV1 implementation.         [ref]
+# ===========================================================================================
+# [ref] RWKV demo: RWKV-LM/RWKV-v7/rwkv_v7_demo.py
+# [ref] TRM base:  trm (2).py
+
+from typing import Tuple, List, Dict, Optional
+from dataclasses import dataclass
+import types
+import math
+import torch
+import torch.nn.functional as F
+from torch import nn
+from pydantic import BaseModel
+import random
+
+# --- your imports (unchanged) --------------------------------------------------------------
+from models.common import trunc_normal_init_
+from models.layers import rms_norm, LinearSwish, SwiGLU, Attention, RotaryEmbedding, CosSin, CastedEmbedding, CastedLinear
+from models.sparse_embedding import CastedSparseEmbedding
+
+IGNORE_LABEL_ID = -100
+
+# ===========================================================================================
+#                               TRM CARRY STRUCTS (unchanged)
+# ===========================================================================================
+
+@dataclass
+class TinyRecursiveReasoningModel_ACTV1InnerCarry:
+    z_H: torch.Tensor
+    z_L: torch.Tensor
+
+
+@dataclass
+class TinyRecursiveReasoningModel_ACTV1Carry:
+    inner_carry: TinyRecursiveReasoningModel_ACTV1InnerCarry
+    
+    steps: torch.Tensor
+    halted: torch.Tensor
+    
+    current_data: Dict[str, torch.Tensor]
+
+# ===========================================================================================
+#                                   CONFIG (NEW FIELDS)
+# ===========================================================================================
+
+class TinyRecursiveReasoningModel_ACTV1Config(BaseModel):
+    batch_size: int
+    seq_len: int
+    puzzle_emb_ndim: int = 0
+    num_puzzle_identifiers: int
+    vocab_size: int
+
+    H_cycles: int
+    L_cycles: int
+
+    H_layers: int # ignored
+    L_layers: int
+
+    # Transformer config
+    hidden_size: int
+    expansion: float
+    num_heads: int
+    pos_encodings: str
+
+    rms_norm_eps: float = 1e-5
+    rope_theta: float = 10000.0
+    
+    # Halting Q-learning config
+    halt_max_steps: int
+    halt_exploration_prob: float
+
+    forward_dtype: str = "bfloat16"
+
+    mlp_t: bool = False # use mlp on L instead of transformer
+    puzzle_emb_len: int = 16 # if non-zero, its specified to this value
+    no_ACT_continue: bool =  True # No continue ACT loss, only use the sigmoid of the halt which makes much more sense
+    
+    # --- NEW: enable RWKV for L-level mixer ---
+    use_rwkv: bool = False
+    rwkv_head_size: int = 64   # per-head size for RWKV TimeMix (hidden_size must be divisible)
+
+# ===========================================================================================
+#                           RWKV-v7 TIME & CHANNEL MIX (adapated)
+#  - Minimal, self-contained PyTorch fallback (no custom CUDA kernels required)
+#  - Based on RWKV-LM/RWKV-v7/rwkv_v7_demo.py: RWKV_Tmix_x070 / RWKV_CMix_x070
+#    (kept faithful but simplified to run without the CUDA op).
+# ===========================================================================================
+# ref: RWKV-LM/RWKV-v7/rwkv_v7_demo.py (classes RWKV_Tmix_x070, RWKV_CMix_x070) :contentReference[oaicite:2]{index=2}
+
+# LoRA-like inner dims used by the demo implementation
+D_DECAY_LORA = 64
+D_AAA_LORA   = 64
+D_MV_LORA    = 32
+D_GATE_LORA  = 128
+
+class _RWKVArgs:
+    def __init__(self, n_embd: int, dim_att: int, dim_ffn: int, head_size_a: int):
+        self.n_embd = n_embd
+        self.dim_att = dim_att
+        self.dim_ffn = dim_ffn
+        self.head_size_a = head_size_a
+
+def _has_rwkv7_kernel():
+    try:
+        # Import rwkv if installed to register custom ops (no-op if absent)
+        try:
+            import rwkv  # noqa: F401
+        except Exception:
+            pass
+        if hasattr(torch.ops, "wkv7") and hasattr(torch.ops.wkv7, "forward"):
+            return True
+        # Try to load a locally built shared library (if present)
+        try:
+            import os, glob
+            candidates = [
+                "/teamspace/studios/this_studio/RWKV-LM/RWKV-v7/cuda/wkv7.so",
+                "/teamspace/studios/this_studio/RWKV-LM/RWKV-v7/cuda/build/wkv7/wkv7.so",
+            ]
+            # Also search common torch extensions dir just in case
+            candidates += glob.glob(os.path.expanduser("~/.cache/torch_extensions/**/wkv7/*.so"), recursive=True)
+            for cand in candidates:
+                if os.path.exists(cand):
+                    torch.ops.load_library(cand)
+                    break
+        except Exception:
+            pass
+        return hasattr(torch.ops, "wkv7") and hasattr(torch.ops.wkv7, "forward")
+    except Exception:
+        return False
+
+_USE_RWKV7_KERNEL = _has_rwkv7_kernel()
+
+@torch._dynamo.disable
+def RWKV7_OP(r, w, k, v, a, b, head_size: int):
+    """
+    Shapes: r,w,k,v,a,b : [B,T,C]; C = H * head_size
+    If CUDA kernel is present (torch.ops.wkv7.forward), use it. Else run portable fallback.
+    """
+    B, T, C = r.size()
+    H = C // head_size
+
+    # ensure contiguous buffers (kernel requires this)
+    r = r.contiguous(); w = w.contiguous(); k = k.contiguous()
+    v = v.contiguous(); a = a.contiguous(); b = b.contiguous()
+
+    if _USE_RWKV7_KERNEL and r.is_cuda:
+        # Kernel expects fp16 tensors; up/downcast around call if needed
+        orig_dtype = r.dtype
+        if orig_dtype != torch.float16:
+            r16 = r.to(torch.float16)
+            w16 = w.to(torch.float16)
+            k16 = k.to(torch.float16)
+            v16 = v.to(torch.float16)
+            a16 = a.to(torch.float16)
+            b16 = b.to(torch.float16)
+        else:
+            r16, w16, k16, v16, a16, b16 = r, w, k, v, a, b
+
+        y16 = torch.empty((B, T, C), device=r.device, dtype=torch.float16)
+        torch.ops.wkv7.forward(B, T, C, H, r16, w16, k16, v16, a16, b16, y16)
+        return y16.to(orig_dtype)
+
+    # ---------- portable Python fallback ----------
+    N = head_size
+    r_ = r.view(B, T, H, N).float()
+    k_ = k.view(B, T, H, N).float()
+    v_ = v.view(B, T, H, N).float()
+    a_ = a.view(B, T, H, N).float()
+    b_ = b.view(B, T, H, N).float()
+    w_ = torch.exp(-torch.exp(w.view(B, T, H, N).float()))
+
+    out = torch.zeros((B, T, H, N), device=r.device, dtype=torch.float)
+    state = torch.zeros((B, H, N, N), device=r.device, dtype=torch.float)
+
+    for t in range(T):
+        kk = k_[:, t, :].view(B, H, 1, N)
+        rr = r_[:, t, :].view(B, H, N, 1)
+        vv = v_[:, t, :].view(B, H, N, 1)
+        aa = a_[:, t, :].view(B, H, N, 1)
+        bb = b_[:, t, :].view(B, H, 1, N)
+        state = state * w_[:, t, :, None, :] + state @ aa @ bb + vv @ kk
+        out[:, t, :] = (state @ rr).view(B, H, N)
+
+    return out.view(B, T, C).to(dtype=r.dtype)
+
+class RWKV_Tmix_x070(nn.Module):
+    """
+    Minimal port of RWKV-7 Goose x070 TimeMix (w/o CUDA kernel), faithful to demo structure.
+    """
+    def __init__(self, args: _RWKVArgs, layer_id: int):
+        super().__init__()
+        self.args = args
+        self.layer_id = layer_id
+
+        self.head_size = args.head_size_a
+        self.n_head = args.dim_att // self.head_size
+        assert args.dim_att % self.n_head == 0
+
+        H = self.n_head
+        C = args.n_embd
+
+        # mixing scalars
+        self.x_r = nn.Parameter(torch.empty(1,1,C))
+        self.x_w = nn.Parameter(torch.empty(1,1,C))
+        self.x_k = nn.Parameter(torch.empty(1,1,C))
+        self.x_v = nn.Parameter(torch.empty(1,1,C))
+        self.x_a = nn.Parameter(torch.empty(1,1,C))
+        self.x_g = nn.Parameter(torch.empty(1,1,C))
+
+        # low-rank parameterizations (as in demo)
+        self.w0 = nn.Parameter(torch.empty(1,1,C))
+        self.w1 = nn.Parameter(torch.empty(C, D_DECAY_LORA))
+        self.w2 = nn.Parameter(torch.empty(D_DECAY_LORA, C))
+
+        self.a0 = nn.Parameter(torch.empty(1,1,C))
+        self.a1 = nn.Parameter(torch.empty(C, D_AAA_LORA))
+        self.a2 = nn.Parameter(torch.empty(D_AAA_LORA, C))
+
+        self.v0 = nn.Parameter(torch.empty(1,1,C))
+        self.v1 = nn.Parameter(torch.empty(C, D_MV_LORA))
+        self.v2 = nn.Parameter(torch.empty(D_MV_LORA, C))
+
+        self.g1 = nn.Parameter(torch.empty(C, D_GATE_LORA))
+        self.g2 = nn.Parameter(torch.empty(D_GATE_LORA, C))
+
+        self.k_k = nn.Parameter(torch.empty(1,1,C))
+        self.k_a = nn.Parameter(torch.empty(1,1,C))
+        self.r_k = nn.Parameter(torch.empty(H, self.head_size))
+
+        self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
+        self.receptance = nn.Linear(C, C, bias=False)
+        self.key = nn.Linear(C, C, bias=False)
+        self.value = nn.Linear(C, C, bias=False)
+        self.output = nn.Linear(C, C, bias=False)
+        self.ln_x = nn.GroupNorm(H, C, eps=64e-5)  # from demo
+
+        # simple init
+        for p in self.parameters():
+            if p.dim() > 1:
+                nn.init.kaiming_uniform_(p, a=math.sqrt(5))
+            else:
+                nn.init.zeros_(p)
+
+    def forward(self, x: torch.Tensor, v_first: Optional[torch.Tensor]):
+        """
+        x: [B, T, C]; v_first: [B, T, C] or None
+        returns: y, v_first_out
+        """
+        B, T, C = x.size()
+        H = self.n_head
+        xx = self.time_shift(x) - x
+
+        xr = x + xx * self.x_r
+        xw = x + xx * self.x_w
+        xk = x + xx * self.x_k
+        xv = x + xx * self.x_v
+        xa = x + xx * self.x_a
+        xg = x + xx * self.x_g
+
+        r = self.receptance(xr)
+        w = -F.softplus(-(self.w0 + torch.tanh(xw @ self.w1) @ self.w2)) - 0.5  # soft-clamp to (-inf, -0.5)
+        k = self.key(xk)
+        v = self.value(xv)
+        if self.layer_id == 0:
+            v_first_out = v  # store v of first layer
+        else:
+            assert v_first is not None, "v_first must be passed across RWKV layers"
+            v = v + (v_first - v) * torch.sigmoid(self.v0 + (xv @ self.v1) @ self.v2)  # value residual
+
+        a = torch.sigmoid(self.a0 + (xa @ self.a1) @ self.a2)  # in-context learning rate
+        g = torch.sigmoid(xg @ self.g1) @ self.g2
+
+        kk = k * self.k_k
+        kk = F.normalize(kk.view(B,T,H,-1), dim=-1, p=2.0).view(B,T,C)
+        k = k * (1 + (a - 1) * self.k_a)
+
+        # RWKV7 operator (auto-dispatch to CUDA kernel if available)
+        x = RWKV7_OP(r, w, k, v, -kk, kk * a, self.head_size)
+        x = self.ln_x(x.view(B * T, C)).view(B, T, C)
+
+        x = x + ((r.view(B,T,H,-1)*k.view(B,T,H,-1)*self.r_k).sum(dim=-1, keepdim=True) * v.view(B,T,H,-1)).view(B,T,C)
+        x = self.output(x * g)
+
+        if self.layer_id == 0:
+            return x, v
+        else:
+            return x, v_first
+
+class RWKV_CMix_x070(nn.Module):
+    """
+    Minimal ChannelMix (FFN) from RWKV-v7 demo (relu^2 gate).
+    """
+    def __init__(self, args: _RWKVArgs, layer_id: int):
+        super().__init__()
+        self.args = args
+        self.layer_id = layer_id
+        self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
+
+        self.x_k = nn.Parameter(torch.empty(1, 1, args.n_embd))
+        self.key = nn.Linear(args.n_embd, args.dim_ffn, bias=False)
+        self.value = nn.Linear(args.dim_ffn, args.n_embd, bias=False)
+        nn.init.zeros_(self.x_k)
+        nn.init.kaiming_uniform_(self.key.weight, a=math.sqrt(5))
+        nn.init.kaiming_uniform_(self.value.weight, a=math.sqrt(5))
+
+    def forward(self, x: torch.Tensor):
+        xx = self.time_shift(x) - x
+        k = x + xx * self.x_k
+        k = torch.relu(self.key(k)) ** 2
+        return self.value(k)
+
+# ===========================================================================================
+#                       TRM L-LEVEL BLOCKS (RWKV vs original)
+# ===========================================================================================
+
+class TinyRecursiveReasoningModel_ACTV1Block(nn.Module):
+    """
+    Original TRM block (unchanged). Uses Attention (if mlp_t=False) + SwiGLU FFN.
+    """
+    def __init__(self, config: TinyRecursiveReasoningModel_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        if self.config.mlp_t:
+            self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size) if self.config.puzzle_emb_len == 0 else self.config.puzzle_emb_len
+            self.mlp_t = SwiGLU(
+                hidden_size=self.config.seq_len + self.puzzle_emb_len, # L
+                expansion=config.expansion,
+            )
+        else:
+            self.self_attn = Attention(
+                hidden_size=config.hidden_size,
+                head_dim=config.hidden_size // config.num_heads,
+                num_heads=config.num_heads,
+                num_key_value_heads=config.num_heads,
+                causal=False
+            )
+        self.mlp = SwiGLU(
+                hidden_size=config.hidden_size,
+                expansion=config.expansion,
+            )
+        self.norm_eps = config.rms_norm_eps
+
+    def forward(self, cos_sin: CosSin, hidden_states: torch.Tensor) -> torch.Tensor:
+        # Post Norm
+        if self.config.mlp_t:
+            hidden_states = hidden_states.transpose(1,2)
+            out = self.mlp_t(hidden_states)
+            hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+            hidden_states = hidden_states.transpose(1,2)
+        else:
+            # Self Attention
+            hidden_states = rms_norm(hidden_states + self.self_attn(cos_sin=cos_sin, hidden_states=hidden_states), variance_epsilon=self.norm_eps)
+        # Fully Connected
+        out = self.mlp(hidden_states)
+        hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+        return hidden_states
+
+class TRM_RWKV_Block(nn.Module):
+    """
+    RWKV-based L-layer for TRM: TimeMix + ChannelMix.
+    - Input / output: [B, L, D]
+    - Threads `v_first` across layers (like RWKV demo).
+    """
+    uses_rwkv = True
+
+    def __init__(self, config: TinyRecursiveReasoningModel_ACTV1Config, layer_id: int):
+        super().__init__()
+        self.config = config
+        d_model = config.hidden_size
+        dim_ffn = int(config.hidden_size * config.expansion)
+        head_size = config.rwkv_head_size
+        assert d_model % head_size == 0, "hidden_size must be divisible by rwkv_head_size"
+
+        args = _RWKVArgs(n_embd=d_model, dim_att=d_model, dim_ffn=dim_ffn, head_size_a=head_size)
+
+        self.ln0 = nn.LayerNorm(d_model) if layer_id == 0 else None
+        self.ln1 = nn.LayerNorm(d_model)
+        self.ln2 = nn.LayerNorm(d_model)
+
+        self.att = RWKV_Tmix_x070(args, layer_id)
+        self.ffn = RWKV_CMix_x070(args, layer_id)
+
+    def forward(self, hidden_states: torch.Tensor, v_first: Optional[torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]:
+        # shapes: [B,L,D]
+        if self.ln0 is not None:
+            hidden_states = self.ln0(hidden_states)
+
+        xx, v_first = self.att(self.ln1(hidden_states), v_first)
+        x = hidden_states + xx
+        x = x + self.ffn(self.ln2(x))
+        return x, v_first
+
+# ===========================================================================================
+#                         REASONING MODULE (threads v_first if RWKV)
+# ===========================================================================================
+
+class TinyRecursiveReasoningModel_ACTV1ReasoningModule(nn.Module):
+    def __init__(self, layers: List[nn.Module]):
+        super().__init__()
+        self.layers = torch.nn.ModuleList(layers)
+
+    def forward(self, hidden_states: torch.Tensor, input_injection: torch.Tensor, **kwargs) -> torch.Tensor:
+        hidden_states = hidden_states + input_injection
+
+        # If this stack is RWKV-based, thread v_first across layers.
+        if len(self.layers) > 0 and getattr(self.layers[0], "uses_rwkv", False):
+            v_first: Optional[torch.Tensor] = None
+            for layer in self.layers:
+                hidden_states, v_first = layer(hidden_states, v_first)
+            return hidden_states
+
+        # Otherwise: original TRM path
+        for layer in self.layers:
+            hidden_states = layer(hidden_states=hidden_states, **kwargs)
+        return hidden_states
+
+# ===========================================================================================
+#                                      INNER MODEL
+# ===========================================================================================
+
+class TinyRecursiveReasoningModel_ACTV1_Inner(nn.Module):
+    def __init__(self, config: TinyRecursiveReasoningModel_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        self.forward_dtype = getattr(torch, self.config.forward_dtype)
+
+        # I/O
+        self.embed_scale = math.sqrt(self.config.hidden_size)
+        embed_init_std = 1.0 / self.embed_scale
+
+        self.embed_tokens = CastedEmbedding(self.config.vocab_size, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+        self.lm_head      = CastedLinear(self.config.hidden_size, self.config.vocab_size, bias=False)
+        self.q_head       = CastedLinear(self.config.hidden_size, 2, bias=True)
+
+        self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size)  if self.config.puzzle_emb_len == 0 else self.config.puzzle_emb_len  # ceil div
+        if self.config.puzzle_emb_ndim > 0:
+            # Zero init puzzle embeddings
+            self.puzzle_emb = CastedSparseEmbedding(self.config.num_puzzle_identifiers, self.config.puzzle_emb_ndim,
+                                                    batch_size=self.config.batch_size, init_std=0, cast_to=self.forward_dtype)
+
+        # Positional encodings (kept for non-RWKV path)
+        if self.config.pos_encodings == "rope":
+            self.rotary_emb = RotaryEmbedding(dim=self.config.hidden_size // self.config.num_heads,
+                                              max_position_embeddings=self.config.seq_len + self.puzzle_emb_len,
+                                              base=self.config.rope_theta)
+        elif self.config.pos_encodings == "learned":
+            self.embed_pos = CastedEmbedding(self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+        else:
+            pass
+
+        # L-level module: either original TRM blocks or RWKV blocks
+        if self.config.use_rwkv:
+            layers = [TRM_RWKV_Block(self.config, layer_id=i) for i in range(self.config.L_layers)]
+        else:
+            layers = [TinyRecursiveReasoningModel_ACTV1Block(self.config) for _ in range(self.config.L_layers)]
+        self.L_level = TinyRecursiveReasoningModel_ACTV1ReasoningModule(layers=layers)
+
+        # Initial states
+        self.H_init = nn.Buffer(trunc_normal_init_(torch.empty(self.config.hidden_size, dtype=self.forward_dtype), std=1), persistent=True)
+        self.L_init = nn.Buffer(trunc_normal_init_(torch.empty(self.config.hidden_size, dtype=self.forward_dtype), std=1), persistent=True)
+
+        # Q head special init (unchanged)
+        with torch.no_grad():
+            self.q_head.weight.zero_()
+            self.q_head.bias.fill_(-5)  # type: ignore
+
+    def _input_embeddings(self, input: torch.Tensor, puzzle_identifiers: torch.Tensor):
+        # Token embedding
+        embedding = self.embed_tokens(input.to(torch.int32))
+
+        # Puzzle embeddings
+        if self.config.puzzle_emb_ndim > 0:
+            puzzle_embedding = self.puzzle_emb(puzzle_identifiers)
+            
+            pad_count = self.puzzle_emb_len * self.config.hidden_size - puzzle_embedding.shape[-1]
+            if pad_count > 0:
+                puzzle_embedding = F.pad(puzzle_embedding, (0, pad_count))
+
+            embedding = torch.cat((puzzle_embedding.view(-1, self.puzzle_emb_len, self.config.hidden_size), embedding), dim=-2)
+
+        # Position embeddings (for non-RWKV path)
+        if (not self.config.use_rwkv) and self.config.pos_encodings == "learned":
+            # scale by 1/sqrt(2) to maintain forward variance
+            embedding = 0.707106781 * (embedding + self.embed_pos.embedding_weight.to(self.forward_dtype))
+
+        # Scale
+        return self.embed_scale * embedding
+
+    def empty_carry(self, batch_size: int):
+        return TinyRecursiveReasoningModel_ACTV1InnerCarry(
+            z_H=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+            z_L=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+        )
+        
+    def reset_carry(self, reset_flag: torch.Tensor, carry: TinyRecursiveReasoningModel_ACTV1InnerCarry):
+        return TinyRecursiveReasoningModel_ACTV1InnerCarry(
+            z_H=torch.where(reset_flag.view(-1, 1, 1), self.H_init, carry.z_H),
+            z_L=torch.where(reset_flag.view(-1, 1, 1), self.L_init, carry.z_L),
+        )
+
+    def forward(self, carry: TinyRecursiveReasoningModel_ACTV1InnerCarry, batch: Dict[str, torch.Tensor]) -> Tuple[TinyRecursiveReasoningModel_ACTV1InnerCarry, torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        seq_info = dict(
+            cos_sin=self.rotary_emb() if (hasattr(self, "rotary_emb") and not self.config.use_rwkv) else None,
+        )
+
+        # Input encoding
+        input_embeddings = self._input_embeddings(batch["inputs"], batch["puzzle_identifiers"])
+
+        # Forward iterations (TRM semantics: commit-last, backprop-last)
+        z_H, z_L = carry.z_H, carry.z_L
+
+        # H_cycles - 1 without grad
+        with torch.no_grad():
+            for _H_step in range(self.config.H_cycles - 1):
+                for _L_step in range(self.config.L_cycles):
+                    z_L = self.L_level(z_L, z_H + input_embeddings, **seq_info)
+                z_H = self.L_level(z_H, z_L, **seq_info)
+
+        # Final H pass with grad
+        for _L_step in range(self.config.L_cycles):
+            z_L = self.L_level(z_L, z_H + input_embeddings, **seq_info)
+        z_H = self.L_level(z_H, z_L, **seq_info)
+
+        # LM Outputs
+        new_carry = TinyRecursiveReasoningModel_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach())  # New carry no grad
+        output = self.lm_head(z_H)[:, self.puzzle_emb_len:]
+        q_logits = self.q_head(z_H[:, 0]).to(torch.float32) # Q-head uses the first puzzle_emb position
+
+        return new_carry, output, (q_logits[..., 0], q_logits[..., 1])
+
+# ===========================================================================================
+#                                    TRM WRAPPER (unchanged)
+# ===========================================================================================
+
+class TinyRecursiveReasoningModel_ACTV1(nn.Module):
+    """ACT wrapper. TRM outer loop unchanged; set cfg.use_rwkv=True to enable RWKV mixer."""
+    def __init__(self, config_dict: dict):
+        super().__init__()
+        self.config = TinyRecursiveReasoningModel_ACTV1Config(**config_dict)
+        self.inner = TinyRecursiveReasoningModel_ACTV1_Inner(self.config)
+
+    @property
+    def puzzle_emb(self):
+        return self.inner.puzzle_emb
+
+    def initial_carry(self, batch: Dict[str, torch.Tensor]):
+        batch_size = batch["inputs"].shape[0]
+        return TinyRecursiveReasoningModel_ACTV1Carry(
+            inner_carry=self.inner.empty_carry(batch_size),  # Empty is expected; reset in first pass as all sequences are halted.
+            steps=torch.zeros((batch_size, ), dtype=torch.int32),
+            halted=torch.ones((batch_size, ), dtype=torch.bool),  # Default to halted
+            current_data={k: torch.empty_like(v) for k, v in batch.items()}
+        )
+        
+    def forward(self, carry: TinyRecursiveReasoningModel_ACTV1Carry, batch: Dict[str, torch.Tensor]) -> Tuple[TinyRecursiveReasoningModel_ACTV1Carry, Dict[str, torch.Tensor]]:
+        # Update data, carry (removing halted sequences)
+        new_inner_carry = self.inner.reset_carry(carry.halted, carry.inner_carry)
+        new_steps = torch.where(carry.halted, 0, carry.steps)
+        new_current_data = {k: torch.where(carry.halted.view((-1, ) + (1, ) * (batch[k].ndim - 1)), batch[k], v) for k, v in carry.current_data.items()}
+
+        # Forward inner model
+        new_inner_carry, logits, (q_halt_logits, q_continue_logits) = self.inner(new_inner_carry, new_current_data)
+
+        outputs = {
+            "logits": logits,
+            "q_halt_logits": q_halt_logits,
+            "q_continue_logits": q_continue_logits
+        }
+
+        with torch.no_grad():
+            # Step
+            new_steps = new_steps + 1
+            is_last_step = new_steps >= self.config.halt_max_steps
+            halted = is_last_step
+
+            # if training, and ACT is enabled
+            if self.training and (self.config.halt_max_steps > 1):
+
+                # Halt signal
+                # NOTE: During evaluation, always use max steps to guarantee same halting inside batch
+                if self.config.no_ACT_continue:
+                    halted = halted | (q_halt_logits > 0)
+                else:
+                    halted = halted | (q_halt_logits > q_continue_logits)
+
+                # Exploration
+                min_halt_steps = (torch.rand_like(q_halt_logits) < self.config.halt_exploration_prob) * torch.randint_like(new_steps, low=2, high=self.config.halt_max_steps + 1)
+                halted = halted & (new_steps >= min_halt_steps)
+
+                if not self.config.no_ACT_continue:
+                    # Compute target Q (simple parallel TD target, like PQN idea)
+                    _, _, (next_q_halt_logits, next_q_continue_logits), _, _ = self.inner(new_inner_carry, new_current_data)
+                    outputs["target_q_continue"] = torch.sigmoid(torch.where(is_last_step, next_q_halt_logits, torch.maximum(next_q_halt_logits, next_q_continue_logits)))
+
+        return TinyRecursiveReasoningModel_ACTV1Carry(new_inner_carry, new_steps, halted, new_current_data), outputs
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_rwkv_sudoku_h32/all_config.yaml b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_rwkv_sudoku_h32/all_config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..931a4e9b6b2a4e3a48cc32a14fb35ab7cd49b887
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_rwkv_sudoku_h32/all_config.yaml
@@ -0,0 +1,48 @@
+arch:
+  H_cycles: 3
+  H_layers: 0
+  L_cycles: 6
+  L_layers: 2
+  expansion: 4
+  forward_dtype: bfloat16
+  halt_exploration_prob: 0.1
+  halt_max_steps: 16
+  hidden_size: 512
+  loss:
+    loss_type: stablemax_cross_entropy
+    name: losses@ACTLossHead
+  mlp_t: false
+  name: recursive_reasoning.trm@TinyRecursiveReasoningModel_ACTV1
+  no_ACT_continue: true
+  num_heads: 8
+  pos_encodings: rope
+  puzzle_emb_len: 16
+  puzzle_emb_ndim: 512
+  rwkv_head_size: 32
+  use_rwkv: true
+beta1: 0.9
+beta2: 0.95
+checkpoint_every_eval: true
+checkpoint_path: checkpoints/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_rwkv_sudoku_h32
+data_paths:
+- data/sudoku-extreme-1k-aug-1000
+data_paths_test: []
+ema: true
+ema_rate: 0.999
+epochs: 50000
+eval_interval: 5000
+eval_save_outputs: []
+evaluators: []
+freeze_weights: false
+global_batch_size: 768
+load_checkpoint: null
+lr: 0.0001
+lr_min_ratio: 1.0
+lr_warmup_steps: 2000
+min_eval_interval: 0
+project_name: Sudoku-extreme-1k-aug-1000-ACT-torch
+puzzle_emb_lr: 0.0001
+puzzle_emb_weight_decay: 1.0
+run_name: pretrain_rwkv_sudoku_h32
+seed: 0
+weight_decay: 1.0
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_rwkv_sudoku_h32/losses.py b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_rwkv_sudoku_h32/losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..cdfb77718bc16f98cdd7da06eb5795c36b866aa7
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_rwkv_sudoku_h32/losses.py
@@ -0,0 +1,102 @@
+from typing import Any, Tuple, Dict, Sequence, Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+import math
+
+IGNORE_LABEL_ID = -100
+
+
+def s(x, epsilon=1e-30):
+    return torch.where(
+        x<0,
+        1/(1-x+ epsilon),
+        x + 1
+    )
+
+
+def log_stablemax(x, dim=-1):
+    s_x = s(x)
+    return torch.log(s_x/torch.sum(s_x, dim=dim, keepdim=True))
+
+
+def stablemax_cross_entropy(logits, labels, ignore_index: int = -100, valid_mask=None):
+    logprobs = log_stablemax(logits.to(torch.float64), dim=-1)
+
+    if valid_mask is None:
+        valid_mask = (labels != ignore_index)
+    transformed_labels = torch.where(valid_mask, labels, 0)
+    prediction_logprobs = torch.gather(logprobs, index=transformed_labels.to(torch.long).unsqueeze(-1), dim=-1).squeeze(-1)
+
+    return -torch.where(valid_mask, prediction_logprobs, 0)
+
+
+def softmax_cross_entropy(logits, labels, ignore_index: int = -100):
+    # Cast logits to f32
+    # Flatten logits
+    return F.cross_entropy(logits.to(torch.float32).view(-1, logits.shape[-1]), labels.to(torch.long).view(-1), ignore_index=ignore_index, reduction="none").view(labels.shape)
+
+
+class ACTLossHead(nn.Module):
+    def __init__(self, model: nn.Module, loss_type: str):
+        super().__init__()
+        self.model = model
+        self.loss_fn = globals()[loss_type]
+        
+    def initial_carry(self, *args, **kwargs):
+        return self.model.initial_carry(*args, **kwargs)  # type: ignore
+
+    def forward(
+        self,
+        return_keys: Sequence[str],
+        # Model args
+        **model_kwargs,
+    ) -> Tuple[Any, torch.Tensor, Dict[str, torch.Tensor], Optional[Dict[str, torch.Tensor]], torch.Tensor]:
+        # Model logits
+        # B x SeqLen x D
+        new_carry, outputs = self.model(**model_kwargs)
+        labels = new_carry.current_data["labels"]
+
+        with torch.no_grad():
+            # Preds
+            outputs["preds"] = torch.argmax(outputs["logits"], dim=-1)
+
+            # Correctness
+            mask = (labels != IGNORE_LABEL_ID)
+            loss_counts = mask.sum(-1)
+            loss_divisor = loss_counts.clamp_min(1).unsqueeze(-1)  # Avoid NaNs in division
+
+            is_correct = mask & (torch.argmax(outputs["logits"], dim=-1) == labels)
+            seq_is_correct = is_correct.sum(-1) == loss_counts
+            
+            # Metrics (halted)
+            valid_metrics = new_carry.halted & (loss_counts > 0)
+            metrics = {
+                "count": valid_metrics.sum(),
+                
+                "accuracy":       torch.where(valid_metrics, (is_correct.to(torch.float32) / loss_divisor).sum(-1), 0).sum(),
+                "exact_accuracy": (valid_metrics & seq_is_correct).sum(),
+
+                "q_halt_accuracy": (valid_metrics & ((outputs["q_halt_logits"] >= 0) == seq_is_correct)).sum(),
+                "steps":          torch.where(valid_metrics, new_carry.steps, 0).sum(),
+            }
+
+        # Losses
+
+        lm_loss = (self.loss_fn(outputs["logits"], labels, ignore_index=IGNORE_LABEL_ID, valid_mask=mask) / loss_divisor).sum()
+        q_halt_loss = F.binary_cross_entropy_with_logits(outputs["q_halt_logits"], seq_is_correct.to(outputs["q_halt_logits"].dtype), reduction="sum")
+        metrics.update({
+            "lm_loss": lm_loss.detach(),
+            "q_halt_loss": q_halt_loss.detach(),
+        })
+        # Q continue (bootstrapping target loss); Alexia: This fits Q-learning, but seems totally unecessary
+        q_continue_loss = 0
+        if "target_q_continue" in outputs:
+            q_continue_loss = F.binary_cross_entropy_with_logits(outputs["q_continue_logits"], outputs["target_q_continue"], reduction="sum")
+
+            metrics["q_continue_loss"] = q_continue_loss.detach()
+        # Filter outputs for return
+        detached_outputs = {k: outputs[k].detach() for k in return_keys if k in outputs}
+
+        return new_carry, lm_loss + 0.5 * (q_halt_loss + q_continue_loss), metrics, detached_outputs, new_carry.halted.all()
diff --git a/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_rwkv_sudoku_h32/trm.py b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_rwkv_sudoku_h32/trm.py
new file mode 100644
index 0000000000000000000000000000000000000000..50dc3f76e33d82269c2929124ddeb9f61bc0f33b
--- /dev/null
+++ b/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_rwkv_sudoku_h32/trm.py
@@ -0,0 +1,603 @@
+# === Tiny Recursive Reasoning Model (TRM) with optional RWKV mixer ==========================
+# - Keeps TRM's commit-last / backprop-last semantics.
+# - Adds RWKV-v7 TimeMix + ChannelMix as an alternative L-level mixer.
+# - Defaults remain identical to your original file unless cfg.use_rwkv=True.
+#
+# RWKV parts adapted from: RWKV-LM/RWKV-v7/rwkv_v7_demo.py (TimeMix & ChannelMix).  [ref]
+# TRM base taken from your TinyRecursiveReasoningModel_ACTV1 implementation.         [ref]
+# ===========================================================================================
+# [ref] RWKV demo: RWKV-LM/RWKV-v7/rwkv_v7_demo.py
+# [ref] TRM base:  trm (2).py
+
+from typing import Tuple, List, Dict, Optional
+from dataclasses import dataclass
+import types
+import math
+import torch
+import torch.nn.functional as F
+from torch import nn
+from pydantic import BaseModel
+import random
+
+# --- your imports (unchanged) --------------------------------------------------------------
+from models.common import trunc_normal_init_
+from models.layers import rms_norm, LinearSwish, SwiGLU, Attention, RotaryEmbedding, CosSin, CastedEmbedding, CastedLinear
+from models.sparse_embedding import CastedSparseEmbedding
+
+IGNORE_LABEL_ID = -100
+
+# ===========================================================================================
+#                               TRM CARRY STRUCTS (unchanged)
+# ===========================================================================================
+
+@dataclass
+class TinyRecursiveReasoningModel_ACTV1InnerCarry:
+    z_H: torch.Tensor
+    z_L: torch.Tensor
+
+
+@dataclass
+class TinyRecursiveReasoningModel_ACTV1Carry:
+    inner_carry: TinyRecursiveReasoningModel_ACTV1InnerCarry
+    
+    steps: torch.Tensor
+    halted: torch.Tensor
+    
+    current_data: Dict[str, torch.Tensor]
+
+# ===========================================================================================
+#                                   CONFIG (NEW FIELDS)
+# ===========================================================================================
+
+class TinyRecursiveReasoningModel_ACTV1Config(BaseModel):
+    batch_size: int
+    seq_len: int
+    puzzle_emb_ndim: int = 0
+    num_puzzle_identifiers: int
+    vocab_size: int
+
+    H_cycles: int
+    L_cycles: int
+
+    H_layers: int # ignored
+    L_layers: int
+
+    # Transformer config
+    hidden_size: int
+    expansion: float
+    num_heads: int
+    pos_encodings: str
+
+    rms_norm_eps: float = 1e-5
+    rope_theta: float = 10000.0
+    
+    # Halting Q-learning config
+    halt_max_steps: int
+    halt_exploration_prob: float
+
+    forward_dtype: str = "bfloat16"
+
+    mlp_t: bool = False # use mlp on L instead of transformer
+    puzzle_emb_len: int = 16 # if non-zero, its specified to this value
+    no_ACT_continue: bool =  True # No continue ACT loss, only use the sigmoid of the halt which makes much more sense
+    
+    # --- NEW: enable RWKV for L-level mixer ---
+    use_rwkv: bool = False
+    rwkv_head_size: int = 64   # per-head size for RWKV TimeMix (hidden_size must be divisible)
+
+# ===========================================================================================
+#                           RWKV-v7 TIME & CHANNEL MIX (adapated)
+#  - Minimal, self-contained PyTorch fallback (no custom CUDA kernels required)
+#  - Based on RWKV-LM/RWKV-v7/rwkv_v7_demo.py: RWKV_Tmix_x070 / RWKV_CMix_x070
+#    (kept faithful but simplified to run without the CUDA op).
+# ===========================================================================================
+# ref: RWKV-LM/RWKV-v7/rwkv_v7_demo.py (classes RWKV_Tmix_x070, RWKV_CMix_x070) :contentReference[oaicite:2]{index=2}
+
+# LoRA-like inner dims used by the demo implementation
+D_DECAY_LORA = 64
+D_AAA_LORA   = 64
+D_MV_LORA    = 32
+D_GATE_LORA  = 128
+
+class _RWKVArgs:
+    def __init__(self, n_embd: int, dim_att: int, dim_ffn: int, head_size_a: int):
+        self.n_embd = n_embd
+        self.dim_att = dim_att
+        self.dim_ffn = dim_ffn
+        self.head_size_a = head_size_a
+
+def _has_rwkv7_kernel():
+    try:
+        # Import rwkv if installed to register custom ops (no-op if absent)
+        try:
+            import rwkv  # noqa: F401
+        except Exception:
+            pass
+        if hasattr(torch.ops, "wkv7") and hasattr(torch.ops.wkv7, "forward"):
+            return True
+        # Try to load a locally built shared library (if present)
+        try:
+            import os, glob
+            # Prefer N=32 build if present (works with rwkv_head_size=32)
+            candidates = [
+                "/teamspace/studios/this_studio/RWKV-LM/RWKV-v7/cuda/build32/wkv7_32/wkv7_32.so",
+                "/teamspace/studios/this_studio/RWKV-LM/RWKV-v7/cuda/build/wkv7/wkv7.so",
+                "/teamspace/studios/this_studio/RWKV-LM/RWKV-v7/cuda/wkv7.so",
+            ]
+            # Also search common torch extensions dir just in case
+            candidates += glob.glob(os.path.expanduser("~/.cache/torch_extensions/**/wkv7/*.so"), recursive=True)
+            for cand in candidates:
+                if os.path.exists(cand):
+                    torch.ops.load_library(cand)
+                    break
+        except Exception:
+            pass
+        return hasattr(torch.ops, "wkv7") and hasattr(torch.ops.wkv7, "forward")
+    except Exception:
+        return False
+
+_USE_RWKV7_KERNEL = _has_rwkv7_kernel()
+
+@torch._dynamo.disable
+def RWKV7_OP(r, w, k, v, a, b, head_size: int):
+    """
+    Shapes: r,w,k,v,a,b : [B,T,C]; C = H * head_size
+    If CUDA kernel is present (torch.ops.wkv7.forward), use it. Else run portable fallback.
+    """
+    B, T, C = r.size()
+    H = C // head_size
+
+    # ensure contiguous buffers (kernel requires this)
+    r = r.contiguous(); w = w.contiguous(); k = k.contiguous()
+    v = v.contiguous(); a = a.contiguous(); b = b.contiguous()
+
+    if _USE_RWKV7_KERNEL and r.is_cuda:
+        # Kernel expects fp16 tensors; up/downcast around call if needed
+        orig_dtype = r.dtype
+        if orig_dtype != torch.float16:
+            r16 = r.to(torch.float16)
+            w16 = w.to(torch.float16)
+            k16 = k.to(torch.float16)
+            v16 = v.to(torch.float16)
+            a16 = a.to(torch.float16)
+            b16 = b.to(torch.float16)
+        else:
+            r16, w16, k16, v16, a16, b16 = r, w, k, v, a, b
+
+        y16 = torch.empty((B, T, C), device=r.device, dtype=torch.float16)
+        torch.ops.wkv7.forward(B, T, C, H, r16, w16, k16, v16, a16, b16, y16)
+        return y16.to(orig_dtype)
+
+    # ---------- portable Python fallback ----------
+    N = head_size
+    r_ = r.view(B, T, H, N).float()
+    k_ = k.view(B, T, H, N).float()
+    v_ = v.view(B, T, H, N).float()
+    a_ = a.view(B, T, H, N).float()
+    b_ = b.view(B, T, H, N).float()
+    w_ = torch.exp(-torch.exp(w.view(B, T, H, N).float()))
+
+    out = torch.zeros((B, T, H, N), device=r.device, dtype=torch.float)
+    state = torch.zeros((B, H, N, N), device=r.device, dtype=torch.float)
+
+    for t in range(T):
+        kk = k_[:, t, :].view(B, H, 1, N)
+        rr = r_[:, t, :].view(B, H, N, 1)
+        vv = v_[:, t, :].view(B, H, N, 1)
+        aa = a_[:, t, :].view(B, H, N, 1)
+        bb = b_[:, t, :].view(B, H, 1, N)
+        state = state * w_[:, t, :, None, :] + state @ aa @ bb + vv @ kk
+        out[:, t, :] = (state @ rr).view(B, H, N)
+
+    return out.view(B, T, C).to(dtype=r.dtype)
+
+class RWKV_Tmix_x070(nn.Module):
+    """
+    Minimal port of RWKV-7 Goose x070 TimeMix (w/o CUDA kernel), faithful to demo structure.
+    """
+    def __init__(self, args: _RWKVArgs, layer_id: int):
+        super().__init__()
+        self.args = args
+        self.layer_id = layer_id
+
+        self.head_size = args.head_size_a
+        self.n_head = args.dim_att // self.head_size
+        assert args.dim_att % self.n_head == 0
+
+        H = self.n_head
+        C = args.n_embd
+
+        # mixing scalars
+        self.x_r = nn.Parameter(torch.empty(1,1,C))
+        self.x_w = nn.Parameter(torch.empty(1,1,C))
+        self.x_k = nn.Parameter(torch.empty(1,1,C))
+        self.x_v = nn.Parameter(torch.empty(1,1,C))
+        self.x_a = nn.Parameter(torch.empty(1,1,C))
+        self.x_g = nn.Parameter(torch.empty(1,1,C))
+
+        # low-rank parameterizations (as in demo)
+        self.w0 = nn.Parameter(torch.empty(1,1,C))
+        self.w1 = nn.Parameter(torch.empty(C, D_DECAY_LORA))
+        self.w2 = nn.Parameter(torch.empty(D_DECAY_LORA, C))
+
+        self.a0 = nn.Parameter(torch.empty(1,1,C))
+        self.a1 = nn.Parameter(torch.empty(C, D_AAA_LORA))
+        self.a2 = nn.Parameter(torch.empty(D_AAA_LORA, C))
+
+        self.v0 = nn.Parameter(torch.empty(1,1,C))
+        self.v1 = nn.Parameter(torch.empty(C, D_MV_LORA))
+        self.v2 = nn.Parameter(torch.empty(D_MV_LORA, C))
+
+        self.g1 = nn.Parameter(torch.empty(C, D_GATE_LORA))
+        self.g2 = nn.Parameter(torch.empty(D_GATE_LORA, C))
+
+        self.k_k = nn.Parameter(torch.empty(1,1,C))
+        self.k_a = nn.Parameter(torch.empty(1,1,C))
+        self.r_k = nn.Parameter(torch.empty(H, self.head_size))
+
+        self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
+        self.receptance = nn.Linear(C, C, bias=False)
+        self.key = nn.Linear(C, C, bias=False)
+        self.value = nn.Linear(C, C, bias=False)
+        self.output = nn.Linear(C, C, bias=False)
+        self.ln_x = nn.GroupNorm(H, C, eps=64e-5)  # from demo
+
+        # simple init
+        for p in self.parameters():
+            if p.dim() > 1:
+                nn.init.kaiming_uniform_(p, a=math.sqrt(5))
+            else:
+                nn.init.zeros_(p)
+
+    def forward(self, x: torch.Tensor, v_first: Optional[torch.Tensor]):
+        """
+        x: [B, T, C]; v_first: [B, T, C] or None
+        returns: y, v_first_out
+        """
+        B, T, C = x.size()
+        H = self.n_head
+        xx = self.time_shift(x) - x
+
+        xr = x + xx * self.x_r
+        xw = x + xx * self.x_w
+        xk = x + xx * self.x_k
+        xv = x + xx * self.x_v
+        xa = x + xx * self.x_a
+        xg = x + xx * self.x_g
+
+        r = self.receptance(xr)
+        w = -F.softplus(-(self.w0 + torch.tanh(xw @ self.w1) @ self.w2)) - 0.5  # soft-clamp to (-inf, -0.5)
+        k = self.key(xk)
+        v = self.value(xv)
+        if self.layer_id == 0:
+            v_first_out = v  # store v of first layer
+        else:
+            assert v_first is not None, "v_first must be passed across RWKV layers"
+            v = v + (v_first - v) * torch.sigmoid(self.v0 + (xv @ self.v1) @ self.v2)  # value residual
+
+        a = torch.sigmoid(self.a0 + (xa @ self.a1) @ self.a2)  # in-context learning rate
+        g = torch.sigmoid(xg @ self.g1) @ self.g2
+
+        kk = k * self.k_k
+        kk = F.normalize(kk.view(B,T,H,-1), dim=-1, p=2.0).view(B,T,C)
+        k = k * (1 + (a - 1) * self.k_a)
+
+        # RWKV7 operator (auto-dispatch to CUDA kernel if available)
+        x = RWKV7_OP(r, w, k, v, -kk, kk * a, self.head_size)
+        x = self.ln_x(x.view(B * T, C)).view(B, T, C)
+
+        x = x + ((r.view(B,T,H,-1)*k.view(B,T,H,-1)*self.r_k).sum(dim=-1, keepdim=True) * v.view(B,T,H,-1)).view(B,T,C)
+        x = self.output(x * g)
+
+        if self.layer_id == 0:
+            return x, v
+        else:
+            return x, v_first
+
+class RWKV_CMix_x070(nn.Module):
+    """
+    Minimal ChannelMix (FFN) from RWKV-v7 demo (relu^2 gate).
+    """
+    def __init__(self, args: _RWKVArgs, layer_id: int):
+        super().__init__()
+        self.args = args
+        self.layer_id = layer_id
+        self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
+
+        self.x_k = nn.Parameter(torch.empty(1, 1, args.n_embd))
+        self.key = nn.Linear(args.n_embd, args.dim_ffn, bias=False)
+        self.value = nn.Linear(args.dim_ffn, args.n_embd, bias=False)
+        nn.init.zeros_(self.x_k)
+        nn.init.kaiming_uniform_(self.key.weight, a=math.sqrt(5))
+        nn.init.kaiming_uniform_(self.value.weight, a=math.sqrt(5))
+
+    def forward(self, x: torch.Tensor):
+        xx = self.time_shift(x) - x
+        k = x + xx * self.x_k
+        k = torch.relu(self.key(k)) ** 2
+        return self.value(k)
+
+# ===========================================================================================
+#                       TRM L-LEVEL BLOCKS (RWKV vs original)
+# ===========================================================================================
+
+class TinyRecursiveReasoningModel_ACTV1Block(nn.Module):
+    """
+    Original TRM block (unchanged). Uses Attention (if mlp_t=False) + SwiGLU FFN.
+    """
+    def __init__(self, config: TinyRecursiveReasoningModel_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        if self.config.mlp_t:
+            self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size) if self.config.puzzle_emb_len == 0 else self.config.puzzle_emb_len
+            self.mlp_t = SwiGLU(
+                hidden_size=self.config.seq_len + self.puzzle_emb_len, # L
+                expansion=config.expansion,
+            )
+        else:
+            self.self_attn = Attention(
+                hidden_size=config.hidden_size,
+                head_dim=config.hidden_size // config.num_heads,
+                num_heads=config.num_heads,
+                num_key_value_heads=config.num_heads,
+                causal=False
+            )
+        self.mlp = SwiGLU(
+                hidden_size=config.hidden_size,
+                expansion=config.expansion,
+            )
+        self.norm_eps = config.rms_norm_eps
+
+    def forward(self, cos_sin: CosSin, hidden_states: torch.Tensor) -> torch.Tensor:
+        # Post Norm
+        if self.config.mlp_t:
+            hidden_states = hidden_states.transpose(1,2)
+            out = self.mlp_t(hidden_states)
+            hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+            hidden_states = hidden_states.transpose(1,2)
+        else:
+            # Self Attention
+            hidden_states = rms_norm(hidden_states + self.self_attn(cos_sin=cos_sin, hidden_states=hidden_states), variance_epsilon=self.norm_eps)
+        # Fully Connected
+        out = self.mlp(hidden_states)
+        hidden_states = rms_norm(hidden_states + out, variance_epsilon=self.norm_eps)
+        return hidden_states
+
+class TRM_RWKV_Block(nn.Module):
+    """
+    RWKV-based L-layer for TRM: TimeMix + ChannelMix.
+    - Input / output: [B, L, D]
+    - Threads `v_first` across layers (like RWKV demo).
+    """
+    uses_rwkv = True
+
+    def __init__(self, config: TinyRecursiveReasoningModel_ACTV1Config, layer_id: int):
+        super().__init__()
+        self.config = config
+        d_model = config.hidden_size
+        dim_ffn = int(config.hidden_size * config.expansion)
+        head_size = config.rwkv_head_size
+        assert d_model % head_size == 0, "hidden_size must be divisible by rwkv_head_size"
+
+        args = _RWKVArgs(n_embd=d_model, dim_att=d_model, dim_ffn=dim_ffn, head_size_a=head_size)
+
+        self.ln0 = nn.LayerNorm(d_model) if layer_id == 0 else None
+        self.ln1 = nn.LayerNorm(d_model)
+        self.ln2 = nn.LayerNorm(d_model)
+
+        self.att = RWKV_Tmix_x070(args, layer_id)
+        self.ffn = RWKV_CMix_x070(args, layer_id)
+
+    def forward(self, hidden_states: torch.Tensor, v_first: Optional[torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]:
+        # shapes: [B,L,D]
+        if self.ln0 is not None:
+            hidden_states = self.ln0(hidden_states)
+
+        xx, v_first = self.att(self.ln1(hidden_states), v_first)
+        x = hidden_states + xx
+        x = x + self.ffn(self.ln2(x))
+        return x, v_first
+
+# ===========================================================================================
+#                         REASONING MODULE (threads v_first if RWKV)
+# ===========================================================================================
+
+class TinyRecursiveReasoningModel_ACTV1ReasoningModule(nn.Module):
+    def __init__(self, layers: List[nn.Module]):
+        super().__init__()
+        self.layers = torch.nn.ModuleList(layers)
+
+    def forward(self, hidden_states: torch.Tensor, input_injection: torch.Tensor, **kwargs) -> torch.Tensor:
+        hidden_states = hidden_states + input_injection
+
+        # If this stack is RWKV-based, thread v_first across layers.
+        if len(self.layers) > 0 and getattr(self.layers[0], "uses_rwkv", False):
+            v_first: Optional[torch.Tensor] = None
+            for layer in self.layers:
+                hidden_states, v_first = layer(hidden_states, v_first)
+            return hidden_states
+
+        # Otherwise: original TRM path
+        for layer in self.layers:
+            hidden_states = layer(hidden_states=hidden_states, **kwargs)
+        return hidden_states
+
+# ===========================================================================================
+#                                      INNER MODEL
+# ===========================================================================================
+
+class TinyRecursiveReasoningModel_ACTV1_Inner(nn.Module):
+    def __init__(self, config: TinyRecursiveReasoningModel_ACTV1Config) -> None:
+        super().__init__()
+        self.config = config
+        self.forward_dtype = getattr(torch, self.config.forward_dtype)
+
+        # I/O
+        self.embed_scale = math.sqrt(self.config.hidden_size)
+        embed_init_std = 1.0 / self.embed_scale
+
+        self.embed_tokens = CastedEmbedding(self.config.vocab_size, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+        self.lm_head      = CastedLinear(self.config.hidden_size, self.config.vocab_size, bias=False)
+        self.q_head       = CastedLinear(self.config.hidden_size, 2, bias=True)
+
+        self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size)  if self.config.puzzle_emb_len == 0 else self.config.puzzle_emb_len  # ceil div
+        if self.config.puzzle_emb_ndim > 0:
+            # Zero init puzzle embeddings
+            self.puzzle_emb = CastedSparseEmbedding(self.config.num_puzzle_identifiers, self.config.puzzle_emb_ndim,
+                                                    batch_size=self.config.batch_size, init_std=0, cast_to=self.forward_dtype)
+
+        # Positional encodings (kept for non-RWKV path)
+        if self.config.pos_encodings == "rope":
+            self.rotary_emb = RotaryEmbedding(dim=self.config.hidden_size // self.config.num_heads,
+                                              max_position_embeddings=self.config.seq_len + self.puzzle_emb_len,
+                                              base=self.config.rope_theta)
+        elif self.config.pos_encodings == "learned":
+            self.embed_pos = CastedEmbedding(self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
+        else:
+            pass
+
+        # L-level module: either original TRM blocks or RWKV blocks
+        if self.config.use_rwkv:
+            layers = [TRM_RWKV_Block(self.config, layer_id=i) for i in range(self.config.L_layers)]
+        else:
+            layers = [TinyRecursiveReasoningModel_ACTV1Block(self.config) for _ in range(self.config.L_layers)]
+        self.L_level = TinyRecursiveReasoningModel_ACTV1ReasoningModule(layers=layers)
+
+        # Initial states
+        self.H_init = nn.Buffer(trunc_normal_init_(torch.empty(self.config.hidden_size, dtype=self.forward_dtype), std=1), persistent=True)
+        self.L_init = nn.Buffer(trunc_normal_init_(torch.empty(self.config.hidden_size, dtype=self.forward_dtype), std=1), persistent=True)
+
+        # Q head special init (unchanged)
+        with torch.no_grad():
+            self.q_head.weight.zero_()
+            self.q_head.bias.fill_(-5)  # type: ignore
+
+    def _input_embeddings(self, input: torch.Tensor, puzzle_identifiers: torch.Tensor):
+        # Token embedding
+        embedding = self.embed_tokens(input.to(torch.int32))
+
+        # Puzzle embeddings
+        if self.config.puzzle_emb_ndim > 0:
+            puzzle_embedding = self.puzzle_emb(puzzle_identifiers)
+            
+            pad_count = self.puzzle_emb_len * self.config.hidden_size - puzzle_embedding.shape[-1]
+            if pad_count > 0:
+                puzzle_embedding = F.pad(puzzle_embedding, (0, pad_count))
+
+            embedding = torch.cat((puzzle_embedding.view(-1, self.puzzle_emb_len, self.config.hidden_size), embedding), dim=-2)
+
+        # Position embeddings (for non-RWKV path)
+        if (not self.config.use_rwkv) and self.config.pos_encodings == "learned":
+            # scale by 1/sqrt(2) to maintain forward variance
+            embedding = 0.707106781 * (embedding + self.embed_pos.embedding_weight.to(self.forward_dtype))
+
+        # Scale
+        return self.embed_scale * embedding
+
+    def empty_carry(self, batch_size: int):
+        return TinyRecursiveReasoningModel_ACTV1InnerCarry(
+            z_H=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+            z_L=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
+        )
+        
+    def reset_carry(self, reset_flag: torch.Tensor, carry: TinyRecursiveReasoningModel_ACTV1InnerCarry):
+        return TinyRecursiveReasoningModel_ACTV1InnerCarry(
+            z_H=torch.where(reset_flag.view(-1, 1, 1), self.H_init, carry.z_H),
+            z_L=torch.where(reset_flag.view(-1, 1, 1), self.L_init, carry.z_L),
+        )
+
+    def forward(self, carry: TinyRecursiveReasoningModel_ACTV1InnerCarry, batch: Dict[str, torch.Tensor]) -> Tuple[TinyRecursiveReasoningModel_ACTV1InnerCarry, torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        seq_info = dict(
+            cos_sin=self.rotary_emb() if (hasattr(self, "rotary_emb") and not self.config.use_rwkv) else None,
+        )
+
+        # Input encoding
+        input_embeddings = self._input_embeddings(batch["inputs"], batch["puzzle_identifiers"])
+
+        # Forward iterations (TRM semantics: commit-last, backprop-last)
+        z_H, z_L = carry.z_H, carry.z_L
+
+        # H_cycles - 1 without grad
+        with torch.no_grad():
+            for _H_step in range(self.config.H_cycles - 1):
+                for _L_step in range(self.config.L_cycles):
+                    z_L = self.L_level(z_L, z_H + input_embeddings, **seq_info)
+                z_H = self.L_level(z_H, z_L, **seq_info)
+
+        # Final H pass with grad
+        for _L_step in range(self.config.L_cycles):
+            z_L = self.L_level(z_L, z_H + input_embeddings, **seq_info)
+        z_H = self.L_level(z_H, z_L, **seq_info)
+
+        # LM Outputs
+        new_carry = TinyRecursiveReasoningModel_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach())  # New carry no grad
+        output = self.lm_head(z_H)[:, self.puzzle_emb_len:]
+        q_logits = self.q_head(z_H[:, 0]).to(torch.float32) # Q-head uses the first puzzle_emb position
+
+        return new_carry, output, (q_logits[..., 0], q_logits[..., 1])
+
+# ===========================================================================================
+#                                    TRM WRAPPER (unchanged)
+# ===========================================================================================
+
+class TinyRecursiveReasoningModel_ACTV1(nn.Module):
+    """ACT wrapper. TRM outer loop unchanged; set cfg.use_rwkv=True to enable RWKV mixer."""
+    def __init__(self, config_dict: dict):
+        super().__init__()
+        self.config = TinyRecursiveReasoningModel_ACTV1Config(**config_dict)
+        self.inner = TinyRecursiveReasoningModel_ACTV1_Inner(self.config)
+
+    @property
+    def puzzle_emb(self):
+        return self.inner.puzzle_emb
+
+    def initial_carry(self, batch: Dict[str, torch.Tensor]):
+        batch_size = batch["inputs"].shape[0]
+        return TinyRecursiveReasoningModel_ACTV1Carry(
+            inner_carry=self.inner.empty_carry(batch_size),  # Empty is expected; reset in first pass as all sequences are halted.
+            steps=torch.zeros((batch_size, ), dtype=torch.int32),
+            halted=torch.ones((batch_size, ), dtype=torch.bool),  # Default to halted
+            current_data={k: torch.empty_like(v) for k, v in batch.items()}
+        )
+        
+    def forward(self, carry: TinyRecursiveReasoningModel_ACTV1Carry, batch: Dict[str, torch.Tensor]) -> Tuple[TinyRecursiveReasoningModel_ACTV1Carry, Dict[str, torch.Tensor]]:
+        # Update data, carry (removing halted sequences)
+        new_inner_carry = self.inner.reset_carry(carry.halted, carry.inner_carry)
+        new_steps = torch.where(carry.halted, 0, carry.steps)
+        new_current_data = {k: torch.where(carry.halted.view((-1, ) + (1, ) * (batch[k].ndim - 1)), batch[k], v) for k, v in carry.current_data.items()}
+
+        # Forward inner model
+        new_inner_carry, logits, (q_halt_logits, q_continue_logits) = self.inner(new_inner_carry, new_current_data)
+
+        outputs = {
+            "logits": logits,
+            "q_halt_logits": q_halt_logits,
+            "q_continue_logits": q_continue_logits
+        }
+
+        with torch.no_grad():
+            # Step
+            new_steps = new_steps + 1
+            is_last_step = new_steps >= self.config.halt_max_steps
+            halted = is_last_step
+
+            # if training, and ACT is enabled
+            if self.training and (self.config.halt_max_steps > 1):
+
+                # Halt signal
+                # NOTE: During evaluation, always use max steps to guarantee same halting inside batch
+                if self.config.no_ACT_continue:
+                    halted = halted | (q_halt_logits > 0)
+                else:
+                    halted = halted | (q_halt_logits > q_continue_logits)
+
+                # Exploration
+                min_halt_steps = (torch.rand_like(q_halt_logits) < self.config.halt_exploration_prob) * torch.randint_like(new_steps, low=2, high=self.config.halt_max_steps + 1)
+                halted = halted & (new_steps >= min_halt_steps)
+
+                if not self.config.no_ACT_continue:
+                    # Compute target Q (simple parallel TD target, like PQN idea)
+                    _, _, (next_q_halt_logits, next_q_continue_logits), _, _ = self.inner(new_inner_carry, new_current_data)
+                    outputs["target_q_continue"] = torch.sigmoid(torch.where(is_last_step, next_q_halt_logits, torch.maximum(next_q_halt_logits, next_q_continue_logits)))
+
+        return TinyRecursiveReasoningModel_ACTV1Carry(new_inner_carry, new_steps, halted, new_current_data), outputs