@inproceedings{wang-etal-2026-outcome,
title = "Outcome Accuracy is Not Enough: Aligning the Reasoning Process of Reward Models",
author = "Wang, Binghai and
Liu, Yantao and
Liu, Yuxuan and
Tang, Tianyi and
Wang, Shenzhi and
Gao, Chang and
Zheng, Chujie and
Zhang, Yichang and
Yu, Le and
Liu, Shixuan and
Gui, Tao and
Zhang, Qi and
Huang, Xuanjing and
Yu, Bowen and
Huang, Fei and
Lin, Junyang",
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.1924/",
pages = "41482--41508",
ISBN = "979-8-89176-390-6",
abstract = "Generative Reward Models (GenRMs) and LLM-as-a-Judge exhibit deceptive alignment by producing correct judgments for incorrect reasons, as they are trained and evaluated to prioritize \textit{Outcome Accuracy}, which undermines their ability to generalize during RLHF. We introduce \textit{Rationale Consistency}, a fine-grained metric that quantifies the alignment between the model{'}s reasoning process and human judgment. Our evaluation of frontier models reveals that rationale consistency effectively discriminates among state-of-the-art models and detects deceptive alignment, while outcome accuracy falls short in both respects. To mitigate this gap, we introduce a hybrid signal that combines rationale consistency with outcome accuracy for GenRM training. Our training method achieves state-of-the-art performance on RM-Bench (87.1{\%}) and JudgeBench (82{\%}), surpassing outcome-only baselines by an average of 5{\%}. Using RM during RLHF, our method effectively improves performance as demonstrated on Arena Hard v2, notably yielding a 7{\%} improvement in creative writing tasks. Further analysis confirms that our method escapes the deceptive alignment trap, effectively reversing the decline in rationale consistency observed in outcome-only training."
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<abstract>Generative Reward Models (GenRMs) and LLM-as-a-Judge exhibit deceptive alignment by producing correct judgments for incorrect reasons, as they are trained and evaluated to prioritize Outcome Accuracy, which undermines their ability to generalize during RLHF. We introduce Rationale Consistency, a fine-grained metric that quantifies the alignment between the model’s reasoning process and human judgment. Our evaluation of frontier models reveals that rationale consistency effectively discriminates among state-of-the-art models and detects deceptive alignment, while outcome accuracy falls short in both respects. To mitigate this gap, we introduce a hybrid signal that combines rationale consistency with outcome accuracy for GenRM training. Our training method achieves state-of-the-art performance on RM-Bench (87.1%) and JudgeBench (82%), surpassing outcome-only baselines by an average of 5%. Using RM during RLHF, our method effectively improves performance as demonstrated on Arena Hard v2, notably yielding a 7% improvement in creative writing tasks. Further analysis confirms that our method escapes the deceptive alignment trap, effectively reversing the decline in rationale consistency observed in outcome-only training.</abstract>
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%0 Conference Proceedings
%T Outcome Accuracy is Not Enough: Aligning the Reasoning Process of Reward Models
%A Wang, Binghai
%A Liu, Yantao
%A Liu, Yuxuan
%A Tang, Tianyi
%A Wang, Shenzhi
%A Gao, Chang
%A Zheng, Chujie
%A Zhang, Yichang
%A Yu, Le
%A Liu, Shixuan
%A Gui, Tao
%A Zhang, Qi
%A Huang, Xuanjing
%A Yu, Bowen
%A Huang, Fei
%A Lin, Junyang
%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 wang-etal-2026-outcome
%X Generative Reward Models (GenRMs) and LLM-as-a-Judge exhibit deceptive alignment by producing correct judgments for incorrect reasons, as they are trained and evaluated to prioritize Outcome Accuracy, which undermines their ability to generalize during RLHF. We introduce Rationale Consistency, a fine-grained metric that quantifies the alignment between the model’s reasoning process and human judgment. Our evaluation of frontier models reveals that rationale consistency effectively discriminates among state-of-the-art models and detects deceptive alignment, while outcome accuracy falls short in both respects. To mitigate this gap, we introduce a hybrid signal that combines rationale consistency with outcome accuracy for GenRM training. Our training method achieves state-of-the-art performance on RM-Bench (87.1%) and JudgeBench (82%), surpassing outcome-only baselines by an average of 5%. Using RM during RLHF, our method effectively improves performance as demonstrated on Arena Hard v2, notably yielding a 7% improvement in creative writing tasks. Further analysis confirms that our method escapes the deceptive alignment trap, effectively reversing the decline in rationale consistency observed in outcome-only training.
%U https://aclanthology.org/2026.acl-long.1924/
%P 41482-41508
Markdown (Informal)
[Outcome Accuracy is Not Enough: Aligning the Reasoning Process of Reward Models](https://aclanthology.org/2026.acl-long.1924/) (Wang et al., ACL 2026)
ACL
- Binghai Wang, Yantao Liu, Yuxuan Liu, Tianyi Tang, Shenzhi Wang, Chang Gao, Chujie Zheng, Yichang Zhang, Le Yu, Shixuan Liu, Tao Gui, Qi Zhang, Xuanjing Huang, Bowen Yu, Fei Huang, and Junyang Lin. 2026. Outcome Accuracy is Not Enough: Aligning the Reasoning Process of Reward Models. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 41482–41508, San Diego, California, United States. Association for Computational Linguistics.