45 lines
2.1 KiB
Markdown
45 lines
2.1 KiB
Markdown
---
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license: mit
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base_model:
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- Qwen/Qwen3-VL-8B-Instruct
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---
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## Model Summary
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`UnifiedReward-2.0-qwen3vl-8b` is the first unified reward model based on [Qwen/Qwen3-VL-8B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct) for multimodal understanding and generation assessment, enabling both pairwise ranking and pointwise scoring, which can be employed for vision model preference alignment.
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For further details, please refer to the following resources:
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- 📰 Paper: https://arxiv.org/pdf/2503.05236
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- 🪐 Project Page: https://codegoat24.github.io/UnifiedReward/
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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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## 🏁 Compared with Current Reward Models
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| Reward Model | Method| Image Generation | Image Understanding | Video Generation | Video Understanding
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| :-----: | :-----: |:-----: |:-----: | :-----: | :-----: |
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| [PickScore](https://github.com/yuvalkirstain/PickScore) |Point | √ | | ||
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| [HPS](https://github.com/tgxs002/HPSv2) | Point | √ | |||
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| [ImageReward](https://github.com/THUDM/ImageReward) | Point| √| |||
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| [LLaVA-Critic](https://huggingface.co/lmms-lab/llava-critic-7b) | Pair/Point | | √ |||
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| [IXC-2.5-Reward](https://github.com/InternLM/InternLM-XComposer) | Pair/Point | | √ ||√|
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| [VideoScore](https://github.com/TIGER-AI-Lab/VideoScore) | Point | | |√ ||
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| [LiFT](https://github.com/CodeGoat24/LiFT) | Point | | |√| |
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| [VisionReward](https://github.com/THUDM/VisionReward) | Point |√ | |√||
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| [VideoReward](https://github.com/KwaiVGI/VideoAlign) | Point | | |√ ||
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| UnifiedReward (Ours) | Pair/Point | √ | √ |√|√|
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## Citation
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```
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@article{unifiedreward,
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title={Unified reward model for multimodal understanding and generation},
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author={Wang, Yibin and Zang, Yuhang and Li, Hao and Jin, Cheng and Wang, Jiaqi},
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journal={arXiv preprint arXiv:2503.05236},
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year={2025}
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}
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``` |