This paper introduces RAGEN, a framework for understanding self-evolution in Large Language Model (LLM) agents through multi-turn reinforcement learning. The work explores how LLM agents can improve their performance over multiple interactions using reinforcement learning techniques.
@misc{ragen,title={RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning},author={Wang, Zihan and Wang, Kangrui and Wang, Qineng and Zhang, Pingyue and Li, Linjie and Yang, Zhengyuan and Jin, Xing and Yu, Kefan and Nguyen, Minh Nhat and Liu, Licheng and Gottlieb, Eli and Lu, Yiping and Cho, Kyunghyun and Wu, Jiajun and Fei-Fei, Li and Wang, Lijuan and Choi, Yejin and Li, Manling},year={2025},eprint={2504.20073},archiveprefix={arXiv},primaryclass={cs.LG},url={https://arxiv.org/abs/2504.20073},}