metadata
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.
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
- Code: GitHub - shengjun-zhang/VisualGRPO
Citation
If you find this work helpful for your research, please consider citing:
@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}
}