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--- |
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license: mit |
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base_model: |
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- CodeGoat24/UnifiedReward-2.0-qwen3vl-2b |
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--- |
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## Model Summary |
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`UnifiedReward-Think-qwen3vl-2b` 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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- 📰 Paper: https://arxiv.org/pdf/2505.03318 |
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- 🪐 Project Page: https://codegoat24.github.io/UnifiedReward/think |
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- 🤗 Model Collections: https://huggingface.co/collections/CodeGoat24/unifiedreward-models-67c3008148c3a380d15ac63a |
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- 🤗 Dataset Collections: https://huggingface.co/collections/CodeGoat24/unifiedreward-training-data-67c300d4fd5eff00fa7f1ede |
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- 👋 Point of Contact: [Yibin Wang](https://codegoat24.github.io) |
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🚀 All inference code is provided at [Github](https://github.com/CodeGoat24/UnifiedReward/tree/main/UnifiedReward-Think/inference_qwen/UnifiedReward-Think-qwen3vl-inference). |
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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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``` |
