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--- |
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license: mit |
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datasets: |
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- CodeGoat24/HPD |
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- CodeGoat24/LiFT-HRA |
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- CodeGoat24/OIP |
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- CodeGoat24/EvalMuse |
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- CodeGoat24/ShareGPTVideo-DPO |
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- CodeGoat24/VideoFeedback |
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- CodeGoat24/LLaVA-Critic-113k |
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- CodeGoat24/VideoDPO |
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base_model: |
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- Qwen/Qwen2.5-VL-7B-Instruct |
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--- |
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# UnifiedReward-2.0-qwen-7B |
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We are actively gathering feedback from the community to improve our models. **We welcome your input and encourage you to stay updated through our repository**!! |
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🔥🔥🔥 We release **UnifiedReward-2.0**-qwen-[[3b](https://huggingface.co/CodeGoat24/UnifiedReward-2.0-qwen-3b)/[7b](https://huggingface.co/CodeGoat24/UnifiedReward-2.0-qwen-7b)/[32b](https://huggingface.co/CodeGoat24/UnifiedReward-2.0-qwen-32b)/[72b](https://huggingface.co/CodeGoat24/UnifiedReward-2.0-qwen-72b)]. |
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This version introduces several new capabilities: |
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>1. **Pairwise scoring** for image and video generation assessment on **_Alignment_**, **_Coherence_**, **_Style_** dimensions. |
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> |
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>2. **Pointwise scoring** for image and video generation assessment on **_Alignment_**, **_Coherence/Physics_**, **_Style_** dimensions. |
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Welcome to try the latest version, and the inference code is available at [`here`](https://github.com/CodeGoat24/UnifiedReward/tree/main/inference_qwen/UnifiedReward-2.0-inference). |
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## Model Summary |
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`UnifiedReward-2.0-qwen-7b` is the first unified reward model based on [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-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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``` |
