@inproceedings{wang-etal-2026-learning-user,
title = "Learning User Simulators with {T}uring Rewards",
author = "Wang, Yingshan Susan and
Zhang, Cedegao E. and
Qiu, Linlu and
He, Zexue and
Li, Pengyuan and
Pentland, Alex and
Levy, Roger P. and
Kim, Yoon",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1393/",
pages = "31797--31844",
ISBN = "979-8-89176-459-0",
abstract = "Learning to simulate human users in interactive settings could advance the training of agents that serve humans, the evaluation of personalization systems, and research in the social sciences. 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 a Turing Test approach for learning user simulator models, where a model is trained via reinforcement learning to produce responses indistinguishable from what the user could have said. Our discriminative Turing reward uses an LLM judge to score how indistinguishable a generated response is from the real user{'}s given the user{'}s history. Across two substantially different domains, namely conversational chat and Reddit forum discussion, we find that the Turing reward consistently outperforms baseline methods on both LLM and human evaluation metrics. Our study suggests that optimizing for indistinguishability, rather than response matching, is an effective route to learning user simulators."
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<abstract>Learning to simulate human users in interactive settings could advance the training of agents that serve humans, the evaluation of personalization systems, and research in the social sciences. 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 a Turing Test approach for learning user simulator models, where a model is trained via reinforcement learning to produce responses indistinguishable from what the user could have said. Our discriminative Turing reward uses an LLM judge to score how indistinguishable a generated response is from the real user’s given the user’s history. Across two substantially different domains, namely conversational chat and Reddit forum discussion, we find that the Turing reward consistently outperforms baseline methods on both LLM and human evaluation metrics. Our study suggests that optimizing for indistinguishability, rather than response matching, is an effective route to learning user simulators.</abstract>
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%0 Conference Proceedings
%T Learning User Simulators with Turing Rewards
%A Wang, Yingshan Susan
%A Zhang, Cedegao E.
%A Qiu, Linlu
%A He, Zexue
%A Li, Pengyuan
%A Pentland, Alex
%A Levy, Roger P.
%A Kim, Yoon
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wang-etal-2026-learning-user
%X Learning to simulate human users in interactive settings could advance the training of agents that serve humans, the evaluation of personalization systems, and research in the social sciences. 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 a Turing Test approach for learning user simulator models, where a model is trained via reinforcement learning to produce responses indistinguishable from what the user could have said. Our discriminative Turing reward uses an LLM judge to score how indistinguishable a generated response is from the real user’s given the user’s history. Across two substantially different domains, namely conversational chat and Reddit forum discussion, we find that the Turing reward consistently outperforms baseline methods on both LLM and human evaluation metrics. Our study suggests that optimizing for indistinguishability, rather than response matching, is an effective route to learning user simulators.
%U https://aclanthology.org/2026.emnlp-main.1393/
%P 31797-31844
Markdown (Informal)
[Learning User Simulators with Turing Rewards](https://aclanthology.org/2026.emnlp-main.1393/) (Wang et al., EMNLP 2026)
ACL
- Yingshan Susan Wang, Cedegao E. Zhang, Linlu Qiu, Zexue He, Pengyuan Li, Alex Pentland, Roger P. Levy, and Yoon Kim. 2026. Learning User Simulators with Turing Rewards. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 31797–31844, Budapest, Hungary. Association for Computational Linguistics.