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README.md
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`Unified-Reward-Think-7b` is the first unified multimodal CoT reward model, capable of multi-dimensional, step-by-step long-chain reasoning for both visual understanding and generation reward tasks.
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For further details, please refer to the following resources:
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- πͺ Project Page: https://codegoat24.github.io/UnifiedReward/think
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- π€ Model Collections: https://huggingface.co/collections/CodeGoat24/unifiedreward-models-67c3008148c3a380d15ac63a
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- π€ Dataset Collections: https://huggingface.co/collections/CodeGoat24/unifiedreward-training-data-67c300d4fd5eff00fa7f1ede
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## Citation
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```
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@article{UnifiedReward,
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title={Unified Multimodal Chain-of-Thought Reward Model through Reinforcement Fine-Tuning.},
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author={Wang, Yibin and Li, Zhimin and Zang, Yuhang and Wang, Chunyu and Lu, Qinglin and Jin, Cheng and Wang, Jiaqi},
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journal={arXiv preprint arXiv:},
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year={2025}
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}
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```
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`Unified-Reward-Think-7b` is the first unified multimodal CoT reward model, capable of multi-dimensional, step-by-step long-chain reasoning for both visual understanding and generation reward tasks.
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For further details, please refer to the following resources:
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- π° Paper: https://arxiv.org/pdf/2505.03318
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- πͺ Project Page: https://codegoat24.github.io/UnifiedReward/think
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- π€ Model Collections: https://huggingface.co/collections/CodeGoat24/unifiedreward-models-67c3008148c3a380d15ac63a
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- π€ Dataset Collections: https://huggingface.co/collections/CodeGoat24/unifiedreward-training-data-67c300d4fd5eff00fa7f1ede
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## Citation
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```
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@article{UnifiedReward-Think,
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title={Unified Multimodal Chain-of-Thought Reward Model through Reinforcement Fine-Tuning.},
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author={Wang, Yibin and Li, Zhimin and Zang, Yuhang and Wang, Chunyu and Lu, Qinglin, and Jin, Cheng and Wang, Jiaqi},
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journal={arXiv preprint arXiv:2505.03318},
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year={2025}
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}
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```
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