| | --- |
| | 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) |
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| | Code: https://github.com/tulerfeng/OneThinker |
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| | ## About OneThinker |
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| | <div align="center"> |
| | <img src="https://github.com/tulerfeng/OneThinker/blob/main/assets/teaser.png?raw=true" alt="OneThinker Teaser" width="95%"> |
| | </div> |
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| | 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**. |
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| | 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. |
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| | 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**. |
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