Quantifying the Utility of User Simulators for Building Collaborative LLM Assistants
User simulators are increasingly leveraged to build interactive AI assistants, yet how to measure the quality of these simulators remains an open question. In this work, we show how simulator quality can be quantified in terms of its downstream utility: how an LLM assistant trained with this user simulator performs in the wild when interacting with real humans. In a controlled experiment where only the user simulator varies, we train LLM assistants via reinforcement learning against a spectrum of simulators, from an LLM prompted to role-play a user to one fine-tuned on human utterances from WildChat. As evaluation, we measure pairwise win rates in a user study with 283 participants and on...
| Authors | Joseph Suh, Ayush Raj, Minwoo Kang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 10 May 2026 |
| Last updated at source | 10 May 2026 |
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