User simulators are gaining attention as environments to train LLM assistants that can better cope with the nuances of multi-turn conversations with real users.
Prior work on user simulation has emphasized generating realistic user utterances, leaving unexplored their potential as a source of direct feedback on the assistant's responses.
Such evaluation signals may not be explicitly communicated in language, but can instead reside in the latent reasoning of users.
In this work
We introduce user simulators with intrinsic feedback, namely, models equipped with latent evaluation states: internal thoughts, reactions, and scalar ratings that evaluate the assistant from a first-person user perspective at each conversation turn.
We train LLM-based simulators using interaction logs between real users and assistants and demonstrate that intrinsic feedback improves simulation realism and critiquing behaviors versus prompted simulator baselines, while also providing turn-level evaluations that existing fine-tuned simulators lack.
Further, evaluations generated by our trained simulators correlate better with human judgments than those of prompted simulators of the same size.
Lastly
Our experiments reveal that simulators with intrinsic feedback provide effective reward signals for multi-turn RL training, matching setups that rely on external judge LLMs for rewards while cutting RL compute by more than half.