File size: 2,290 Bytes
e0bfa9b 23f84d9 e0bfa9b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 |
---
task_categories:
- image-text-to-text
- video-text-to-text
- object-detection
- image-segmentation
language:
- en
---
This repository contains the evaluation data presented in: [OneThinker: All-in-one Reasoning Model for Image and Video](https://arxiv.org/abs/2512.03043)
Project Page: https://github.com/tulerfeng/OneThinker
Code: https://github.com/tulerfeng/OneThinker
## About OneThinker
<div align="center">
<img src="https://github.com/tulerfeng/OneThinker/blob/main/assets/teaser.png?raw=true" alt="OneThinker Teaser" width="95%">
</div>
We introduce **OneThinker**, an all-in-one multimodal reasoning generalist that is **capable of thinking across a wide range of fundamental visual tasks within a single model**.
We construct the large-scale **OneThinker-600k** multi-task training corpus and build **OneThinker-SFT-340k** with high-quality CoT annotations for cold-start SFT. Moreover, we propose **EMA-GRPO**, a new RL method that **balances heterogeneous reward signals across diverse visual tasks**, via simply tracking task-wise moving averages of reward std.
OneThinker demonstrates **strong performance on 31 benchmarks across 10 fundamental vision tasks**, while showing cross-task knowledge transfer and promising zero-shot generalization toward a **unified multimodal reasoning generalist**.
All code, models, and data are fully released.
## Dataset
Our dataset covers both image and video modalities and spans a series of fundamental visual reasoning tasks, including rule-based QA, open-ended QA, captioning, spatial grounding, temporal grounding, spatio-temporal grounding, tracking, and segmentation
<div align="center">
<img src="https://github.com/tulerfeng/OneThinker/blob/main/assets/dataset.png?raw=true" alt="OneThinker Dataset Overview" width="90%">
</div>
To enable effective SFT initialization for reasoning, we leverage a strong proprietary model, Seed1.5-VL to produce CoT annotations.
The `onethinker_rl_train.json` file is for RL training while `onethinker_sft_image.json` and `onethinker_sft_video.json` is for SFT cold start. The json files end with `_unsampled` are unsampled full set.
## Sample Usage
For inference on a single example, you may refer to:
```bash
python ./Evaluation/inference_single/inference.py
``` |