MIMESIS: Learning User Simulators as Training Environments for Interactive Agents
Quick summary
arXiv:2610.09484v2 Announce Type: replace Abstract: Training and evaluating interactive language agents typically requires rich user interactions, yet collecting human feedback is expensive and difficult to scale. Simulated users offer a scalable alternative, but they must both resemble real user behavior and provide useful learning experiences for agents. In contrast, most agent-training frameworks rely on off-the-shelf assistant LLMs, whose helpfulness can make them overly cooperative, explicit, and behaviorally homogeneous compared with real users. We introduce MIMESIS, a purpose-built user
Key takeaways
- arXiv:2610.09484v2 Announce Type: replace Abstract: Training and evaluating interactive language agents typically requires rich user interactions, yet collecting human feedback is expensive and difficult to scale.
- Simulated users offer a scalable alternative, but they must both resemble real user behavior and provide useful learning experiences for agents.
- In contrast, most agent-training frameworks rely on off-the-shelf assistant LLMs, whose helpfulness can make them overly cooperative, explicit, and behaviorally homogeneous compared with real users.
Why it matters
“MIMESIS: Learning User Simulators as Training Environments for Interactive Agents” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.
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