---
license: apache-2.0
pipeline_tag: text-to-image
---

# E-GRPO: High Entropy Steps Drive Effective Reinforcement Learning for Flow Models

This repository contains the weights for E-GRPO, as presented in the paper [E-GRPO: High Entropy Steps Drive Effective Reinforcement Learning for Flow Models](https://huggingface.co/papers/2601.00423).

## Introduction

E-GRPO (Entropy-Guided Group Relative Policy Optimization) is a reinforcement learning approach designed to enhance flow-matching models for human preference alignment. The key insight is that high-entropy denoising steps are more critical for policy optimization. The authors propose a merging-step strategy that focuses training on these important steps, leading to more efficient and effective exploration compared to standard SDE or ODE sampling methods.

## Resources

- **Paper:** [E-GRPO: High Entropy Steps Drive Effective Reinforcement Learning for Flow Models](https://huggingface.co/papers/2601.00423)
- **Code:** [GitHub - shengjun-zhang/VisualGRPO](https://github.com/shengjun-zhang/VisualGRPO)

## Citation

If you find this work helpful for your research, please consider citing:

```bibtex
@article{zhang2025egrpo,
  title={E-GRPO: High Entropy Steps Drive Effective Reinforcement Learning for Flow Models},
  author={Zhang, Shengjun and Zhang, Zhang and Dai, Chensheng and Duan, Yueqi},
  journal={arXiv preprint arXiv:2601.00423},
  year={2025}
}
```