@inproceedings{peng-etal-2026-wildreward,
title = "{W}ild{R}eward: Learning Reward Models from In-the-Wild Human Interactions",
author = "Peng, Hao and
Qi, Yunjia and
Wang, Xiaozhi and
Yao, Zijun and
Hou, Lei and
Li, Juanzi",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.864/",
pages = "18932--18948",
ISBN = "979-8-89176-390-6",
abstract = "Reward models (RMs) are crucial for the training of large language models (LLMs), yet they typically rely on large-scale human-annotated preference pairs. With the widespread deployment of LLMs, in-the-wild interactions have emerged as a rich source of implicit reward signals. This raises the question: Can we develop reward models directly from in-the-wild interactions? In this work, we explore this possibility by adopting WildChat as an interaction source and proposing a pipeline to extract reliable human feedback, yielding 186k high-quality instances for training WildReward via ordinal regression directly on user feedback without preference pairs. Extensive experiments demonstrate that WildReward achieves comparable or even superior performance compared to conventional reward models, with improved calibration and cross-sample consistency. We also observe that WildReward benefits directly from user diversity, where more users yield stronger reward models. Finally, we apply WildReward to online DPO training and observe significant improvements across various downstream tasks. We will release our code, data, and models to facilitate future research."
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%0 Conference Proceedings
%T WildReward: Learning Reward Models from In-the-Wild Human Interactions
%A Peng, Hao
%A Qi, Yunjia
%A Wang, Xiaozhi
%A Yao, Zijun
%A Hou, Lei
%A Li, Juanzi
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F peng-etal-2026-wildreward
%X Reward models (RMs) are crucial for the training of large language models (LLMs), yet they typically rely on large-scale human-annotated preference pairs. With the widespread deployment of LLMs, in-the-wild interactions have emerged as a rich source of implicit reward signals. This raises the question: Can we develop reward models directly from in-the-wild interactions? In this work, we explore this possibility by adopting WildChat as an interaction source and proposing a pipeline to extract reliable human feedback, yielding 186k high-quality instances for training WildReward via ordinal regression directly on user feedback without preference pairs. Extensive experiments demonstrate that WildReward achieves comparable or even superior performance compared to conventional reward models, with improved calibration and cross-sample consistency. We also observe that WildReward benefits directly from user diversity, where more users yield stronger reward models. Finally, we apply WildReward to online DPO training and observe significant improvements across various downstream tasks. We will release our code, data, and models to facilitate future research.
%U https://aclanthology.org/2026.acl-long.864/
%P 18932-18948
Markdown (Informal)
[WildReward: Learning Reward Models from In-the-Wild Human Interactions](https://aclanthology.org/2026.acl-long.864/) (Peng et al., ACL 2026)
ACL
- Hao Peng, Yunjia Qi, Xiaozhi Wang, Zijun Yao, Lei Hou, and Juanzi Li. 2026. WildReward: Learning Reward Models from In-the-Wild Human Interactions. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 18932–18948, San Diego, California, United States. Association for Computational Linguistics.