EAU: Environment-Agent-User Co-evolution for Training User-Centric Agents

Key points

• Training LLM agents to interact with users over multiple turns is challenging because the learning signal depends not only on the agent, but also on the user simulator and environment, which jointly shape the agent's experience.1
• Simulated users can be stochastic or inconsistent, while environments may contain unreliable tools, incomplete state coverage, and incorrect verifiers.1
• We enable this process through an editable symbolic representation of user and environment dynamics, while the agent remains a neural LLM optimized with RL.1
• Across three interactive-agent benchmarks and two backbone models, EAU consistently outperforms prior RL methods, improving the overall score over the strongest baseline by 14.7--15.0% and increasing successful task completion on ColBench by up to 36.7%, showing that co-evolving the training system with the policy can substantially improve multi-turn RL.1

Sources (1)

• [1]EAU: Environment-Agent-User Co-evolution for Training User-Centric Agents
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 10:11 AM

  We instead introduce EAU, a meta-agent framework for environment--agent--user co-evolution.

  Training LLM agents to interact with users over multiple turns is challenging because the learning signal depends not only on the agent, but also on the user simulator and environment, which jointly shape the agent's experience.

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