README.md

---
license: apache-2.0
language:
- en
tags:
- spatial-reasoning
- multimodal
- vision-language
- scene-graph
- reinforcement-learning
base_model: Qwen/Qwen2.5-VL-3B-Instruct
pipeline_tag: image-text-to-text
---

# SpatialThinker-3B

<p align="center">
  <a href="https://arxiv.org/abs/2511.07403">
    <img src="https://img.shields.io/badge/arXiv-2511.07403-b31b1b.svg" alt="arXiv">
  </a>
  <a href="https://hunarbatra.com/SpatialThinker">
    <img src="https://img.shields.io/badge/🌐%20Project%20Page-blue.svg" alt="Project Page">
  </a>
  <a href="https://github.com/hunarbatra/SpatialThinker">
    <img src="https://img.shields.io/badge/GitHub-Repository-black.svg" alt="GitHub">
  </a>
</p>

**SpatialThinker-3B** is a 3D-aware multimodal large language model (MLLM) trained with reinforcement learning to integrate structured spatial grounding with multi-step reasoning. The model simulates human-like spatial perception by constructing a scene graph of task-relevant objects and spatial relations, and reasoning towards an answer via dense spatial rewards.

## Model Description

- **Base Model**: Qwen2.5-VL-3B-Instruct
- **Training**: GRPO (Group Relative Policy Optimization) with dense spatial rewards
- **Training Data**: STVQA-7K (7,587 spatial VQA samples)
- **Authors**: Hunar Batra, Haoqin Tu, Hardy Chen, Yuanze Lin, Cihang Xie, Ronald Clark
- **Institutions**: University of Oxford, UC Santa Cruz

## Key Features

- **Structured Spatial Reasoning**: Constructs question-focused scene subgraphs with objects, bounding boxes, and relations
- **Dense Spatial Rewards**: Multi-objective reward function enforcing format, count, accuracy, and spatial grounding
- **9 Spatial Reasoning Categories**: Relations, reach, size, orientation, instance location, depth, distance, count, and existence
- **Outperforms GPT-4o**: On spatial understanding benchmarks while using only 7K training samples

## Inference Template

Use the following template for inference:

```
You FIRST observe the image in <observe> </observe> tags, then visualise the relevant scene graph in <scene> </scene> tags, followed by thinking about the reasoning process as an internal monologue within <think> </think> tags and then provide the final answer. The final answer MUST BE put within <answer> </answer> tags, and only return the final choice including the correct option and answer within the answer tags, e.g., <answer> (A) cat </answer>.

Image size: {Width} x {Height}
```

## Usage

```python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from PIL import Image

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "OX-PIXL/SpatialThinker-3B",
    torch_dtype="auto",
    device_map="auto"
)
processor = AutoProcessor.from_pretrained("OX-PIXL/SpatialThinker-3B")

# Load image
image = Image.open("your_image.jpg")
width, height = image.size

# Prepare prompt with template
template = f"""You FIRST observe the image in <observe> </observe> tags, then visualise the relevant scene graph in <scene> </scene> tags, followed by thinking about the reasoning process as an internal monologue within <think> </think> tags and then provide the final answer. The final answer MUST BE put within <answer> </answer> tags, and only return the final choice including the correct option and answer within the answer tags, e.g., <answer> (A) cat </answer>.

Image size: {width} x {height}"""

question = "Where is the cat relative to the couch? (A) on top of (B) in front of (C) behind (D) beside"

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": image},
            {"type": "text", "text": template + "\n\n" + question},
        ],
    }
]

text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device)

generated_ids = model.generate(**inputs, max_new_tokens=1024)
output = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(output)
```

## Citation

```bibtex
@misc{batra2025spatialthinkerreinforcing3dreasoning,  
  title={SpatialThinker: Reinforcing 3D Reasoning in Multimodal LLMs via Spatial Rewards},  
  author={Hunar Batra and Haoqin Tu and Hardy Chen and Yuanze Lin and Cihang Xie and Ronald Clark},  
  year={2025},  
  eprint={2511.07403},  
  archivePrefix={arXiv},  
  primaryClass={cs.CV},  
  url={https://arxiv.org/abs/2511.07403},  
}
```

## Links

- 📄 **Paper**: [arXiv:2511.07403](https://arxiv.org/abs/2511.07403)
- 🌐 **Project Page**: [hunarbatra.com/SpatialThinker](https://hunarbatra.com/SpatialThinker)
- 💻 **GitHub**: [github.com/hunarbatra/SpatialThinker](https://github.com/hunarbatra/SpatialThinker)
- 🤗 **Dataset**: [OX-PIXL/STVQA-7K](https://huggingface.co/datasets/OX-PIXL/STVQA-7K)