@inproceedings{huang-etal-2026-accuracy,
title = "From Accuracy to Robustness: A Study of Rule- and Model-based Verifiers in Mathematical Reasoning",
author = "Huang, Yuzhen and
Zeng, Weihao and
Zeng, Xingshan and
Zhu, Qi and
He, Junxian",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.255/",
pages = "5787--5808",
ISBN = "979-8-89176-459-0",
abstract = "Trustworthy verifiers are essential for the success of reinforcement learning with verifiable reward (RLVR), which is the core methodology behind various large reasoning models such as DeepSeek-R1. In complex domains like mathematical reasoning, rule-based verifiers have been widely adopted in previous works to train strong reasoning models. However, the reliability of these verifiers and their impact on the RL training process remain poorly understood. In this work, we take mathematical reasoning as a case study and conduct a comprehensive analysis of various verifiers in both static evaluation and RL training scenarios. We show that widely used rule-based verifiers fail to recognize equivalent answers in different formats, leading to substantial false negatives that increasingly hinder RL performance as the policy model gets stronger. Model-based verifiers substantially improve static accuracy but are highly susceptible to reward hacking during RL, where they misclassify certain patterns in responses as correct, particularly after fine-tuning. Our findings underscore the challenges inherent to both rule- and model-based verifiers and provide insights toward developing more accurate and robust reward systems for reinforcement learning."
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%0 Conference Proceedings
%T From Accuracy to Robustness: A Study of Rule- and Model-based Verifiers in Mathematical Reasoning
%A Huang, Yuzhen
%A Zeng, Weihao
%A Zeng, Xingshan
%A Zhu, Qi
%A He, Junxian
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F huang-etal-2026-accuracy
%X Trustworthy verifiers are essential for the success of reinforcement learning with verifiable reward (RLVR), which is the core methodology behind various large reasoning models such as DeepSeek-R1. In complex domains like mathematical reasoning, rule-based verifiers have been widely adopted in previous works to train strong reasoning models. However, the reliability of these verifiers and their impact on the RL training process remain poorly understood. In this work, we take mathematical reasoning as a case study and conduct a comprehensive analysis of various verifiers in both static evaluation and RL training scenarios. We show that widely used rule-based verifiers fail to recognize equivalent answers in different formats, leading to substantial false negatives that increasingly hinder RL performance as the policy model gets stronger. Model-based verifiers substantially improve static accuracy but are highly susceptible to reward hacking during RL, where they misclassify certain patterns in responses as correct, particularly after fine-tuning. Our findings underscore the challenges inherent to both rule- and model-based verifiers and provide insights toward developing more accurate and robust reward systems for reinforcement learning.
%U https://aclanthology.org/2026.emnlp-main.255/
%P 5787-5808
Markdown (Informal)
[From Accuracy to Robustness: A Study of Rule- and Model-based Verifiers in Mathematical Reasoning](https://aclanthology.org/2026.emnlp-main.255/) (Huang et al., EMNLP 2026)
ACL