Overview
Field: NLP Authors: Yingshan Susan Wang, Cedegao E. Zhang, Linlu Qiu Published: 2026-06-19 arXiv: 2506.14980
Summary
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 train a large language model (LLM) to match a single ground-truth response, either by maximizing the log probability or by using a similarity reward.
This paper proposes Turing-RL: a Turing-Test-based reinforcement learning approach for training user simulator models. Turing-RL uses a discriminative Turing reward, where an LLM judge scores how indistinguishable a generated response is from the real user's response given the user's history. The user simulator LLM learns to produce responses that are indistinguishable from what the user could have said under this reward.
Results
Across two different domains—conversational chat and Reddit forum discussion—Turing-RL consistently outperforms baseline methods on both LLM and human evaluation metrics. The study suggests that optimizing for indistinguishability, rather than response matching, is effective for learning user simulators.
Original Abstract
> 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.
Paper: arXiv:2506.14980