@inproceedings{lambert-etal-2025-rewardbench,
title = "{R}eward{B}ench: Evaluating Reward Models for Language Modeling",
author = "Lambert, Nathan and
Pyatkin, Valentina and
Morrison, Jacob and
Miranda, LJ and
Lin, Bill Yuchen and
Chandu, Khyathi and
Dziri, Nouha and
Kumar, Sachin and
Zick, Tom and
Choi, Yejin and
Smith, Noah A. and
Hajishirzi, Hannaneh",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.96/",
doi = "10.18653/v1/2025.findings-naacl.96",
pages = "1755--1797",
ISBN = "979-8-89176-195-7",
abstract = "Reward models (RMs) are at the crux of successfully using RLHF to align pretrained models to human preferences, yet there has been relatively little study that focuses on evaluation of those models. Evaluating reward models presents an opportunity to understand the opaque technologies used for alignment of language models and which values are embedded in them. Resources for reward model training and understanding are sparse in the nascent open-source community around them. To enhance scientific understanding of reward models, we present RewardBench, a benchmark dataset and code-base for evaluation. The RewardBench dataset is a collection of prompt-chosen-rejected trios spanning chat, reasoning, and safety, to benchmark how reward models perform on challenging, structured and out-of-distribution queries. We create specific comparison datasets for RMs that have subtle, but verifiable reasons (e.g. bugs, incorrect facts) why one answer should be preferred to another. On the RewardBench leaderboard, we evaluate RMs trained with a variety of methods, such as the direct MLE training of classifiers and the implicit reward modeling of Direct Preference Optimization (DPO). We present many findings on propensity for refusals, reasoning limitations, and instruction following shortcomings of various reward models towards a better understanding of the RLHF process."
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<abstract>Reward models (RMs) are at the crux of successfully using RLHF to align pretrained models to human preferences, yet there has been relatively little study that focuses on evaluation of those models. Evaluating reward models presents an opportunity to understand the opaque technologies used for alignment of language models and which values are embedded in them. Resources for reward model training and understanding are sparse in the nascent open-source community around them. To enhance scientific understanding of reward models, we present RewardBench, a benchmark dataset and code-base for evaluation. The RewardBench dataset is a collection of prompt-chosen-rejected trios spanning chat, reasoning, and safety, to benchmark how reward models perform on challenging, structured and out-of-distribution queries. We create specific comparison datasets for RMs that have subtle, but verifiable reasons (e.g. bugs, incorrect facts) why one answer should be preferred to another. On the RewardBench leaderboard, we evaluate RMs trained with a variety of methods, such as the direct MLE training of classifiers and the implicit reward modeling of Direct Preference Optimization (DPO). We present many findings on propensity for refusals, reasoning limitations, and instruction following shortcomings of various reward models towards a better understanding of the RLHF process.</abstract>
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%0 Conference Proceedings
%T RewardBench: Evaluating Reward Models for Language Modeling
%A Lambert, Nathan
%A Pyatkin, Valentina
%A Morrison, Jacob
%A Miranda, L. J.
%A Lin, Bill Yuchen
%A Chandu, Khyathi
%A Dziri, Nouha
%A Kumar, Sachin
%A Zick, Tom
%A Choi, Yejin
%A Smith, Noah A.
%A Hajishirzi, Hannaneh
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F lambert-etal-2025-rewardbench
%X Reward models (RMs) are at the crux of successfully using RLHF to align pretrained models to human preferences, yet there has been relatively little study that focuses on evaluation of those models. Evaluating reward models presents an opportunity to understand the opaque technologies used for alignment of language models and which values are embedded in them. Resources for reward model training and understanding are sparse in the nascent open-source community around them. To enhance scientific understanding of reward models, we present RewardBench, a benchmark dataset and code-base for evaluation. The RewardBench dataset is a collection of prompt-chosen-rejected trios spanning chat, reasoning, and safety, to benchmark how reward models perform on challenging, structured and out-of-distribution queries. We create specific comparison datasets for RMs that have subtle, but verifiable reasons (e.g. bugs, incorrect facts) why one answer should be preferred to another. On the RewardBench leaderboard, we evaluate RMs trained with a variety of methods, such as the direct MLE training of classifiers and the implicit reward modeling of Direct Preference Optimization (DPO). We present many findings on propensity for refusals, reasoning limitations, and instruction following shortcomings of various reward models towards a better understanding of the RLHF process.
%R 10.18653/v1/2025.findings-naacl.96
%U https://aclanthology.org/2025.findings-naacl.96/
%U https://doi.org/10.18653/v1/2025.findings-naacl.96
%P 1755-1797
Markdown (Informal)
[RewardBench: Evaluating Reward Models for Language Modeling](https://aclanthology.org/2025.findings-naacl.96/) (Lambert et al., Findings 2025)
ACL
- Nathan Lambert, Valentina Pyatkin, Jacob Morrison, LJ Miranda, Bill Yuchen Lin, Khyathi Chandu, Nouha Dziri, Sachin Kumar, Tom Zick, Yejin Choi, Noah A. Smith, and Hannaneh Hajishirzi. 2025. RewardBench: Evaluating Reward Models for Language Modeling. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 1755–1797, Albuquerque, New Mexico. Association for Computational Linguistics.