Add model card for E-GRPO

README.md

@@ -1,3 +1,30 @@
----
-license: apache-2.0
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+---
+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}
+}
+```