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--- |
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library_name: transformers |
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tags: |
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- multimodal |
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- reasoning |
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- sft |
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- rl |
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datasets: |
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- LightChen2333/M3CoT |
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- ModalityDance/Omni-Bench |
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base_model: |
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- GAIR/Anole-7b-v0.1 |
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license: mit |
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pipeline_tag: any-to-any |
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--- |
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# Omni-R1-Zero |
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Omni-R1-Zero is trained without multimodal annotations. It bootstraps step-wise visualizations from text-only CoT seeds, then follows the SFT→RL recipe to learn interleaved multimodal reasoning. |
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<p align="center"> |
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<a href="https://arxiv.org/abs/2601.09536"><b>Paper</b>👁️</a> · |
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<a href="https://github.com/ModalityDance/Omni-R1"><b>Code</b>🐙</a> · |
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<a href="https://huggingface.co/datasets/ModalityDance/Omni-Bench"><b>Omni-Bench</b>🧪</a> |
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</p> |
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## Citation |
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```bibtex |
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@misc{cheng2026omnir1unifiedgenerativeparadigm, |
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title={Omni-R1: Towards the Unified Generative Paradigm for Multimodal Reasoning}, |
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author={Dongjie Cheng and Yongqi Li and Zhixin Ma and Hongru Cai and Yupeng Hu and Wenjie Wang and Liqiang Nie and Wenjie Li}, |
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year={2026}, |
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eprint={2601.09536}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.AI}, |
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url={https://arxiv.org/abs/2601.09536}, |
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} |
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``` |
