@inproceedings{zheng-etal-2026-comprehensive,
title = "A Comprehensive Survey of Process Reward Models: Data Generation, Model Construction, and Usage",
author = "Zheng, Congmin and
Zhu, Jiachen and
Ou, Zhuoying and
Chen, Yuxiang and
Zhang, Kangning and
Shan, Rong and
Zheng, Zeyu and
Yang, Mengyue and
Lin, Jianghao and
Yu, Yong and
Zhang, Weinan",
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.163/",
pages = "3591--3607",
ISBN = "979-8-89176-390-6",
abstract = "Large Language Models (LLMs) have advanced reasoning ability, yet conventional alignment remains dominated by outcome reward models (ORMs) that judge only final answers. Process Reward Models(PRMs) address this gap by evaluating and guiding reasoning at the step or trajectory level. This survey provides a systematic overview of PRMs through the full loop: how to generate process data, build PRMs, and use PRMs for test-time scaling and reinforcement learning. We summarize applications across math, code, text, multimodal reasoning, robotics, and agents, and review emerging benchmarks. Our goal is to clarify design spaces, reveal open challenges, and guide future research toward fine-grained, robust reasoning alignment."
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%0 Conference Proceedings
%T A Comprehensive Survey of Process Reward Models: Data Generation, Model Construction, and Usage
%A Zheng, Congmin
%A Zhu, Jiachen
%A Ou, Zhuoying
%A Chen, Yuxiang
%A Zhang, Kangning
%A Shan, Rong
%A Zheng, Zeyu
%A Yang, Mengyue
%A Lin, Jianghao
%A Yu, Yong
%A Zhang, Weinan
%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 zheng-etal-2026-comprehensive
%X Large Language Models (LLMs) have advanced reasoning ability, yet conventional alignment remains dominated by outcome reward models (ORMs) that judge only final answers. Process Reward Models(PRMs) address this gap by evaluating and guiding reasoning at the step or trajectory level. This survey provides a systematic overview of PRMs through the full loop: how to generate process data, build PRMs, and use PRMs for test-time scaling and reinforcement learning. We summarize applications across math, code, text, multimodal reasoning, robotics, and agents, and review emerging benchmarks. Our goal is to clarify design spaces, reveal open challenges, and guide future research toward fine-grained, robust reasoning alignment.
%U https://aclanthology.org/2026.acl-long.163/
%P 3591-3607
Markdown (Informal)
[A Comprehensive Survey of Process Reward Models: Data Generation, Model Construction, and Usage](https://aclanthology.org/2026.acl-long.163/) (Zheng et al., ACL 2026)
ACL
- Congmin Zheng, Jiachen Zhu, Zhuoying Ou, Yuxiang Chen, Kangning Zhang, Rong Shan, Zeyu Zheng, Mengyue Yang, Jianghao Lin, Yong Yu, and Weinan Zhang. 2026. A Comprehensive Survey of Process Reward Models: Data Generation, Model Construction, and Usage. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3591–3607, San Diego, California, United States. Association for Computational Linguistics.