MIMESIS: Learning User Simulators as Training Environments for Interactive Agents

Key points

• Training and evaluating interactive language agents typically requires rich user interactions, yet collecting human feedback is expensive and difficult to scale.1
• Simulated users offer a scalable alternative, but they must both resemble real user behavior and provide useful learning experiences for agents.1
• Across eight environments, training with MIMESIS yields better agent performance than training with GPT-5.5 under all nine unseen user simulators, demonstrating stronger generalization to new user simulators.1
• Moreover, we propose Coached On-Policy Self-Distillation (CSD), which leverages simulator-generated private reasoning traces and subsequent utterances as feedback on how well the agent addresses user needs.1

Sources (2)

• [1]MIMESIS: Learning User Simulators as Training Environments for Interactive Agents
  Hugging Face Daily Papers · Oct 7, 12:00 AM

  We introduce MIMESIS, a purpose-built user simulator trained on human conversations with explicit reasoning supervision and 13 realistic behavioral patterns derived from real user interactions.

  Training and evaluating interactive language agents typically requires rich user interactions, yet collecting human feedback is expensive and difficult to scale.

• [2]MIMESIS: Learning User Simulators as Training Environments for Interactive Agents
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 05:37 AM · same content

Extractive summary: sentences quoted from the sources.