| --- |
| license: mit |
| base_model: |
| - Qwen/Qwen3-VL-2B-Instruct |
| --- |
| |
|
|
| ## Model Summary |
|
|
| `UnifiedReward-2.0-qwen3vl-2b` is the first unified reward model based on [Qwen/Qwen3-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct) for multimodal understanding and generation assessment, enabling both pairwise ranking and pointwise scoring, which can be employed for vision model preference alignment. |
|
|
| For further details, please refer to the following resources: |
| - 📰 Paper: https://arxiv.org/pdf/2503.05236 |
| - 🪐 Project Page: https://codegoat24.github.io/UnifiedReward/ |
| - 🤗 Model Collections: https://huggingface.co/collections/CodeGoat24/unifiedreward-models-67c3008148c3a380d15ac63a |
| - 🤗 Dataset Collections: https://huggingface.co/collections/CodeGoat24/unifiedreward-training-data-67c300d4fd5eff00fa7f1ede |
| - 👋 Point of Contact: [Yibin Wang](https://codegoat24.github.io) |
|
|
|
|
| ## 🏁 Compared with Current Reward Models |
|
|
| | Reward Model | Method| Image Generation | Image Understanding | Video Generation | Video Understanding |
| | :-----: | :-----: |:-----: |:-----: | :-----: | :-----: | |
| | [PickScore](https://github.com/yuvalkirstain/PickScore) |Point | √ | | || |
| | [HPS](https://github.com/tgxs002/HPSv2) | Point | √ | ||| |
| | [ImageReward](https://github.com/THUDM/ImageReward) | Point| √| ||| |
| | [LLaVA-Critic](https://huggingface.co/lmms-lab/llava-critic-7b) | Pair/Point | | √ ||| |
| | [IXC-2.5-Reward](https://github.com/InternLM/InternLM-XComposer) | Pair/Point | | √ ||√| |
| | [VideoScore](https://github.com/TIGER-AI-Lab/VideoScore) | Point | | |√ || |
| | [LiFT](https://github.com/CodeGoat24/LiFT) | Point | | |√| | |
| | [VisionReward](https://github.com/THUDM/VisionReward) | Point |√ | |√|| |
| | [VideoReward](https://github.com/KwaiVGI/VideoAlign) | Point | | |√ || |
| | UnifiedReward (Ours) | Pair/Point | √ | √ |√|√| |
|
|
|
|
| ## Citation |
|
|
| ``` |
| @article{unifiedreward, |
| title={Unified reward model for multimodal understanding and generation}, |
| author={Wang, Yibin and Zang, Yuhang and Li, Hao and Jin, Cheng and Wang, Jiaqi}, |
| journal={arXiv preprint arXiv:2503.05236}, |
| year={2025} |
| } |
| ``` |
