---
library_name: diffusers
license: mit
pipeline_tag: text-to-video
base_model:
- Wan-AI/Wan2.1-T2V-14B
---

# Model Summary
This model is GRPO trained using [UnifiedReward-Flex](https://huggingface.co/collections/CodeGoat24/unifiedreward-flex) as reward on the training dataset of [UniGenBench](https://github.com/CodeGoat24/UniGenBench).

🚀 The inference code is available at [Github](https://github.com/CodeGoat24/Pref-GRPO/blob/main/inference/wan_dist_infer.sh).


For further details, please refer to the following resources:
- 📰 Paper: https://arxiv.org/abs/2602.02380
- 🪐 Project Page: https://codegoat24.github.io/UnifiedReward/flex
- 🤗 Model Collections: https://huggingface.co/collections/CodeGoat24/unifiedreward-flex
- 🤗 Dataset: https://huggingface.co/datasets/CodeGoat24/UnifiedReward-Flex-SFT-90K
- 👋 Point of Contact: [Yibin Wang](https://codegoat24.github.io)

![image](https://cdn-uploads.huggingface.co/production/uploads/654c6845bac6e6e49895a5b5/dNPdIonOQGSN9o7zx-X_T.png)


![image](https://cdn-uploads.huggingface.co/production/uploads/654c6845bac6e6e49895a5b5/M5TQd9hqPjFSarxv8l2WN.png)

## Citation

```bibtex
@article{unifiedreward-flex,
  title={Unified Personalized Reward Model for Vision Generation},
  author={Wang, Yibin and Zang, Yuhang and Han, Feng and Bu, Jiazi and Zhou, Yujie and Jin, Cheng and Wang, Jiaqi},
  journal={arXiv preprint arXiv:2602.02380},
  year={2026}
}
```