most citedRAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning

3 citations · 5 across the 7 of their papers we have counts for

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cs.LG2026

RAGEN-2: Reasoning Collapse in Agentic RL

Zihan Wang, Chi Gui, Xing Jin +13

RL training of multi-turn LLM agents is inherently unstable, and reasoning quality directly determines task performance. Entropy is widely used to track reasoning stability. Howeve…

cs.LG2025★ 3 cited

RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning

Zihan Wang, Kangrui Wang, Qineng Wang +15

Training large language models (LLMs) as interactive agents presents unique challenges including long-horizon decision making and interacting with stochastic environment feedback.…