Paper: EAU meta-agent jointly manages user-centric agentic RL training
xiye_nlp · x · 2026-10-22
The authors introduce EAU, a meta-agent that auto-manages RL training through Environment-Agent-User co-evolution. Motivation: user-centric agentic RL involves many components—reward functions, user simulators, RL runs—all affecting learning, and coordinating them is non-trivial; training can be unstable especially with LM-simulated users. EAU puts a meta-agent in charge, jointly handling all components and outperforming prior expert-designed pipelines.
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