@inproceedings{chen-etal-2025-better-process,
title = "Better Process Supervision with Bi-directional Rewarding Signals",
author = "Chen, Wenxiang and
He, Wei and
Xi, Zhiheng and
Guo, Honglin and
Hong, Boyang and
Zhang, Jiazheng and
Li, Nijun and
Gui, Tao and
Li, Yun and
Zhang, Qi and
Huang, Xuanjing",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.747/",
doi = "10.18653/v1/2025.findings-acl.747",
pages = "14471--14485",
ISBN = "979-8-89176-256-5",
abstract = "Process supervision, i.e., evaluating each step, is critical for complex large language model (LLM) reasoning and test-time searching with increased inference compute. Existing approaches, represented by process reward models (PRMs), primarily focus on rewarding signals up to the current step, exhibiting a one-directional nature and lacking a mechanism to model the distance to the final target. To address this problem, we draw inspiration from the A* algorithm, which states that an effective supervisory signal should simultaneously consider the incurred cost and the estimated cost for reaching the target. Building on this key insight, we introduce BiRM, a novel process supervision model that not only evaluates the correctness of previous steps but also models the probability of future success. We conduct extensive experiments on mathematical reasoning tasks and demonstrate that BiRM provides more precise evaluations of LLM reasoning steps, achieving an improvement of 3.1{\%} on Gaokao2023 over PRM under the Best-of-N sampling method. Besides, in search-based strategies, BiRM provides more comprehensive guidance and outperforms ORM by 5.0{\%} and PRM by 3.8{\%} respectively on MATH-500."
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<abstract>Process supervision, i.e., evaluating each step, is critical for complex large language model (LLM) reasoning and test-time searching with increased inference compute. Existing approaches, represented by process reward models (PRMs), primarily focus on rewarding signals up to the current step, exhibiting a one-directional nature and lacking a mechanism to model the distance to the final target. To address this problem, we draw inspiration from the A* algorithm, which states that an effective supervisory signal should simultaneously consider the incurred cost and the estimated cost for reaching the target. Building on this key insight, we introduce BiRM, a novel process supervision model that not only evaluates the correctness of previous steps but also models the probability of future success. We conduct extensive experiments on mathematical reasoning tasks and demonstrate that BiRM provides more precise evaluations of LLM reasoning steps, achieving an improvement of 3.1% on Gaokao2023 over PRM under the Best-of-N sampling method. Besides, in search-based strategies, BiRM provides more comprehensive guidance and outperforms ORM by 5.0% and PRM by 3.8% respectively on MATH-500.</abstract>
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%0 Conference Proceedings
%T Better Process Supervision with Bi-directional Rewarding Signals
%A Chen, Wenxiang
%A He, Wei
%A Xi, Zhiheng
%A Guo, Honglin
%A Hong, Boyang
%A Zhang, Jiazheng
%A Li, Nijun
%A Gui, Tao
%A Li, Yun
%A Zhang, Qi
%A Huang, Xuanjing
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F chen-etal-2025-better-process
%X Process supervision, i.e., evaluating each step, is critical for complex large language model (LLM) reasoning and test-time searching with increased inference compute. Existing approaches, represented by process reward models (PRMs), primarily focus on rewarding signals up to the current step, exhibiting a one-directional nature and lacking a mechanism to model the distance to the final target. To address this problem, we draw inspiration from the A* algorithm, which states that an effective supervisory signal should simultaneously consider the incurred cost and the estimated cost for reaching the target. Building on this key insight, we introduce BiRM, a novel process supervision model that not only evaluates the correctness of previous steps but also models the probability of future success. We conduct extensive experiments on mathematical reasoning tasks and demonstrate that BiRM provides more precise evaluations of LLM reasoning steps, achieving an improvement of 3.1% on Gaokao2023 over PRM under the Best-of-N sampling method. Besides, in search-based strategies, BiRM provides more comprehensive guidance and outperforms ORM by 5.0% and PRM by 3.8% respectively on MATH-500.
%R 10.18653/v1/2025.findings-acl.747
%U https://aclanthology.org/2025.findings-acl.747/
%U https://doi.org/10.18653/v1/2025.findings-acl.747
%P 14471-14485
Markdown (Informal)
[Better Process Supervision with Bi-directional Rewarding Signals](https://aclanthology.org/2025.findings-acl.747/) (Chen et al., Findings 2025)
ACL
- Wenxiang Chen, Wei He, Zhiheng Xi, Honglin Guo, Boyang Hong, Jiazheng Zhang, Nijun Li, Tao Gui, Yun Li, Qi Zhang, and Xuanjing Huang. 2025. Better Process Supervision with Bi-directional Rewarding Signals. In Findings of the Association for Computational Linguistics: ACL 2025, pages 14471–14485, Vienna, Austria. Association for Computational Linguistics.