RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning
Zihan Wang, Kangrui Wang, Qineng Wang, Pingyue Zhang, Linjie Li, Zhengyuan Yang, Kefan Yu, Minh Nhat Nguyen, Licheng Liu, Eli Gottlieb, Monica Lam, Yiping Lu, Kyunghyun Cho, Jiajun Wu, Li Fei-Fei, Lijuan Wang, Yejin Choi, Manling Li
2025-04-30
Summary
This paper talks about RAGEN, a new approach for helping AI agents, like chatbots, get better at learning from their own experiences during conversations by using reinforcement learning.
What's the problem?
Training AI agents to interact well over multiple turns in a conversation is tough because they can become unstable or confused about what kinds of responses are actually helpful or correct.
What's the solution?
The researchers created methods called StarPO and RAGEN that use reinforcement learning to guide these AI agents, making the training process more stable and helping the agents understand what kinds of answers get better results in different situations.
Why it matters?
This matters because it helps create smarter, more reliable AI agents that can hold better conversations and adapt to all sorts of real-life situations, making them more useful for things like tutoring, customer service, or even just chatting.
Abstract
StarPO and RAGEN address challenges in training interactive language model agents through RL, improving stability and reward shaping in diverse environments.