---
license: apache-2.0
---


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  <img src="https://cdn-uploads.huggingface.co/production/uploads/623d8ca4c29adf5ef6175615/q3Anm7o-MoNYjB8JztGVT.png" width="60%" />
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<font size=3><div align='center' >  
[[📖 arXiv Paper](https://arxiv.org/abs/2502.10391)] 
[[📊 R1-Reward Code](https://github.com/yfzhang114/r1_reward)] 
[[📝 R1-Reward Data](https://huggingface.co/datasets/yifanzhang114/R1-Reward-RL)] 
</div></font>

# Training Multimodal Reward Model Through Stable Reinforcement Learning

🔥 We are proud to open-source **R1-Reward**, a comprehensive project for improve reward modeling through reinforcement learning. This release includes:

*   **R1-Reward Model:** A state-of-the-art (SOTA) multimodal reward model demonstrating substantial gains (Voting@15):
    *   **13.5%** improvement on VL Reward-Bench.
    *   **3.5%** improvement on MM-RLHF Reward-Bench.
    *   **14.6%** improvement on Multimodal Reward Bench.
*   **StableReinforce Algorithm:** A novel reinforcement learning method that enhances the Reinforce++ approach by improving training loss stability, advantage estimation, and reward function design.
*   **Open-Source Resources:** We provide the R1-Reward model, the R1-Reward RL training dataset, and inference code for IXC-Reward，MM-RLHF Reward and R1-Reward on the three benchmarks in Figure 1.


![image/png](https://cdn-uploads.huggingface.co/production/uploads/623d8ca4c29adf5ef6175615/yW7YWlxhsbLOaX927uG99.png)


## Citation

If you find it useful for your research and applications, please cite related papers/blogs using this BibTeX:
```bibtex
@article{zhang2025r1,
  title={R1-Reward: Training Multimodal Reward Model Through Stable Reinforcement Learning},
  author={Zhang, Yi-Fan and Lu, Xingyu and Hu, Xiao and Fu, Chaoyou and Wen, Bin and Zhang, Tianke and Liu, Changyi and Jiang, Kaiyu and Chen, Kaibing and Tang, Kaiyu and others},
  journal={arXiv preprint arXiv:2505.02835},
  year={2025}
}
```

## Related Projects
- [MM-RLHF: The Next Step Forward in Multimodal LLM Alignment](https://mm-rlhf.github.io/)
- [MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans?](https://github.com/yfzhang114/MME-RealWorld)
- [MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs](https://arxiv.org/abs/2411.15296)
- [Beyond LLaVA-HD: Diving into High-Resolution Large Multimodal Models](https://github.com/yfzhang114/SliME)
- [VITA-1.5: Towards GPT-4o Level Real-Time Vision and Speech Interaction](https://github.com/VITA-MLLM/VITA)