| | --- |
| | 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 |
| | ``` |
