@inproceedings{yu-etal-2025-self,
title = "Self-Generated Critiques Boost Reward Modeling for Language Models",
author = "Yu, Yue and
Chen, Zhengxing and
Zhang, Aston and
Tan, Liang and
Zhu, Chenguang and
Pang, Richard Yuanzhe and
Qian, Yundi and
Wang, Xuewei and
Gururangan, Suchin and
Zhang, Chao and
Kambadur, Melanie and
Mahajan, Dhruv and
Hou, Rui",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.573/",
doi = "10.18653/v1/2025.naacl-long.573",
pages = "11499--11514",
ISBN = "979-8-89176-189-6",
abstract = "Reward modeling is crucial for aligning large language models (LLMs) with human preferences, especially in reinforcement learning from human feedback (RLHF). However, current reward models mainly produce scalar scores and struggle to incorporate critiques in a natural language format. We hypothesize that predicting both critiques and the scalar reward would improve reward modeling ability. Motivated by this, we propose Critic-RM, a framework that improves reward models using self-generated critiques without extra supervision. Critic-RM employs a two-stage process: generating and filtering high-quality critiques, followed by joint fine-tuning on reward prediction and critique generation. Experiments across benchmarks show that Critic-RM improves reward modeling accuracy by 3.7{\%}-7.3{\%} compared to standard reward models and LLM judges, demonstrating strong performance and data efficiency. Additional studies further validate the effectiveness of the generated critiques."
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<abstract>Reward modeling is crucial for aligning large language models (LLMs) with human preferences, especially in reinforcement learning from human feedback (RLHF). However, current reward models mainly produce scalar scores and struggle to incorporate critiques in a natural language format. We hypothesize that predicting both critiques and the scalar reward would improve reward modeling ability. Motivated by this, we propose Critic-RM, a framework that improves reward models using self-generated critiques without extra supervision. Critic-RM employs a two-stage process: generating and filtering high-quality critiques, followed by joint fine-tuning on reward prediction and critique generation. Experiments across benchmarks show that Critic-RM improves reward modeling accuracy by 3.7%-7.3% compared to standard reward models and LLM judges, demonstrating strong performance and data efficiency. Additional studies further validate the effectiveness of the generated critiques.</abstract>
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%0 Conference Proceedings
%T Self-Generated Critiques Boost Reward Modeling for Language Models
%A Yu, Yue
%A Chen, Zhengxing
%A Zhang, Aston
%A Tan, Liang
%A Zhu, Chenguang
%A Pang, Richard Yuanzhe
%A Qian, Yundi
%A Wang, Xuewei
%A Gururangan, Suchin
%A Zhang, Chao
%A Kambadur, Melanie
%A Mahajan, Dhruv
%A Hou, Rui
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F yu-etal-2025-self
%X Reward modeling is crucial for aligning large language models (LLMs) with human preferences, especially in reinforcement learning from human feedback (RLHF). However, current reward models mainly produce scalar scores and struggle to incorporate critiques in a natural language format. We hypothesize that predicting both critiques and the scalar reward would improve reward modeling ability. Motivated by this, we propose Critic-RM, a framework that improves reward models using self-generated critiques without extra supervision. Critic-RM employs a two-stage process: generating and filtering high-quality critiques, followed by joint fine-tuning on reward prediction and critique generation. Experiments across benchmarks show that Critic-RM improves reward modeling accuracy by 3.7%-7.3% compared to standard reward models and LLM judges, demonstrating strong performance and data efficiency. Additional studies further validate the effectiveness of the generated critiques.
%R 10.18653/v1/2025.naacl-long.573
%U https://aclanthology.org/2025.naacl-long.573/
%U https://doi.org/10.18653/v1/2025.naacl-long.573
%P 11499-11514
Markdown (Informal)
[Self-Generated Critiques Boost Reward Modeling for Language Models](https://aclanthology.org/2025.naacl-long.573/) (Yu et al., NAACL 2025)
ACL
- Yue Yu, Zhengxing Chen, Aston Zhang, Liang Tan, Chenguang Zhu, Richard Yuanzhe Pang, Yundi Qian, Xuewei Wang, Suchin Gururangan, Chao Zhang, Melanie Kambadur, Dhruv Mahajan, and Rui Hou. 2025. Self-Generated Critiques Boost Reward Modeling for Language Models. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 11499–11514, Albuquerque, New Mexico. Association for Computational Linguistics.