Learning User Simulators with Turing Rewards
This work addresses the challenge of training more realistic user simulators for interactive AI systems, offering a new training paradigm that improves indistinguishability over standard response-matching approaches.
The authors propose Turing-RL, a reinforcement learning method using a Turing test reward to train user simulator LLMs that produce responses indistinguishable from real users. It outperforms baselines on conversational chat and Reddit forum tasks in both LLM and human evaluations.
Learning to simulate human users in interactive settings could advance the training of agent assistants, evaluation of personalization systems, research in the social sciences, and more. Existing approaches generally do so by training a large language model (LLM) to match a single ground truth response, either by maximizing the log probability or by using a similarity reward. We instead propose {Turing-RL}: a Turing-Test-based reinforcement learning approach for training user simulator models. {Turing-RL} uses a discriminative Turing reward with an LLM judge to score how indistinguishable a generated response is from the real user's given the user's history, and the user simulator LLM learns to produce responses indistinguishable from what the user could have said with such rewards. Across two different domains--conversational chat and Reddit forum discussion--we find that {Turing-RL} consistently outperforms baseline methods on both LLM and human evaluation metrics. Our study suggests that optimizing for indistinguishability, rather than response matching, is effective for learning user simulators.