OneThinker: All-in-one Reasoning Model for Image and Video
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866917441521582080 |
|---|---|
| author | Feng, Kaituo Zhang, Manyuan Li, Hongyu Fan, Kaixuan Chen, Shuang Jiang, Yilei Zheng, Dian Sun, Peiwen Zhang, Yiyuan Sun, Haoze Feng, Yan Pei, Peng Cai, Xunliang Yue, Xiangyu |
| author_facet | Feng, Kaituo Zhang, Manyuan Li, Hongyu Fan, Kaixuan Chen, Shuang Jiang, Yilei Zheng, Dian Sun, Peiwen Zhang, Yiyuan Sun, Haoze Feng, Yan Pei, Peng Cai, Xunliang Yue, Xiangyu |
| contents | Reinforcement learning (RL) has recently achieved remarkable success in eliciting visual reasoning within Multimodal Large Language Models (MLLMs). However, existing approaches typically train separate models for different tasks and treat image and video reasoning as disjoint domains. This results in limited scalability toward a multimodal reasoning generalist, which restricts practical versatility and hinders potential knowledge sharing across tasks and modalities. To this end, we propose OneThinker, an all-in-one reasoning model that unifies image and video understanding across diverse fundamental visual tasks, including question answering, captioning, spatial and temporal grounding, tracking, and segmentation. To achieve this, we construct the OneThinker-600k training corpus covering all these tasks and employ commercial models for CoT annotation, resulting in OneThinker-SFT-340k for SFT cold start. Furthermore, we propose EMA-GRPO to handle reward heterogeneity in multi-task RL by tracking task-wise moving averages of reward standard deviations for balanced optimization. Extensive experiments on diverse visual benchmarks show that OneThinker delivers strong performance on 31 benchmarks, across 10 fundamental visual understanding tasks. Moreover, it exhibits effective knowledge transfer between certain tasks and preliminary zero-shot generalization ability, marking a step toward a unified multimodal reasoning generalist. All code, model, and data are released. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_03043 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OneThinker: All-in-one Reasoning Model for Image and Video Feng, Kaituo Zhang, Manyuan Li, Hongyu Fan, Kaixuan Chen, Shuang Jiang, Yilei Zheng, Dian Sun, Peiwen Zhang, Yiyuan Sun, Haoze Feng, Yan Pei, Peng Cai, Xunliang Yue, Xiangyu Computer Vision and Pattern Recognition Reinforcement learning (RL) has recently achieved remarkable success in eliciting visual reasoning within Multimodal Large Language Models (MLLMs). However, existing approaches typically train separate models for different tasks and treat image and video reasoning as disjoint domains. This results in limited scalability toward a multimodal reasoning generalist, which restricts practical versatility and hinders potential knowledge sharing across tasks and modalities. To this end, we propose OneThinker, an all-in-one reasoning model that unifies image and video understanding across diverse fundamental visual tasks, including question answering, captioning, spatial and temporal grounding, tracking, and segmentation. To achieve this, we construct the OneThinker-600k training corpus covering all these tasks and employ commercial models for CoT annotation, resulting in OneThinker-SFT-340k for SFT cold start. Furthermore, we propose EMA-GRPO to handle reward heterogeneity in multi-task RL by tracking task-wise moving averages of reward standard deviations for balanced optimization. Extensive experiments on diverse visual benchmarks show that OneThinker delivers strong performance on 31 benchmarks, across 10 fundamental visual understanding tasks. Moreover, it exhibits effective knowledge transfer between certain tasks and preliminary zero-shot generalization ability, marking a step toward a unified multimodal reasoning generalist. All code, model, and data are released. |
| title | OneThinker: All-in-one Reasoning Model for Image and Video |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.03043 |