@inproceedings{ye-etal-2026-dont,
title = "Don{'}t Peek at the Answer: Outcome-Masked Group Relative Policy Optimization for Label-Free {RLVR}",
author = "Ye, Yongshi and
Zhang, Liang and
Chen, Yidong and
Shi, Xiaodong and
Fu, Biao",
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.2547/",
pages = "57905--57928",
ISBN = "979-8-89176-459-0",
abstract = "Reinforcement Learning with Verifiable Rewards (RLVR) improves LLM reasoning but typically relies on ground-truth (GT) answers, limiting scalability. Voting-based label-free RLVR replaces gold supervision with answer-level consensus from model samples. However, collapse arises when the same answer-level signal is used both to estimate rewards and to drive token-level policy optimization, encouraging the model to directly reinforce answer tokens rather than improve reasoning. We propose OM-GRPO, a label-free RLVR framework that decouples reward estimation from policy optimization. OM-GRPO masks gradients on the answer span while retaining answer-level rewards through a soft consensus signal, shifting optimization pressure away from answer tokens. We further introduce Contrast-Augmented Reward, which refines reward estimation via low-cost pairwise comparisons over existing trajectories without additional rollouts. Across diverse reasoning benchmarks and three LLM backbones, OM-GRPO outperforms label-free RLVR methods, matches supervised GT-Reward training with stable optimization, and gains 4.24 points over majority voting in Test-Time Training."
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<abstract>Reinforcement Learning with Verifiable Rewards (RLVR) improves LLM reasoning but typically relies on ground-truth (GT) answers, limiting scalability. Voting-based label-free RLVR replaces gold supervision with answer-level consensus from model samples. However, collapse arises when the same answer-level signal is used both to estimate rewards and to drive token-level policy optimization, encouraging the model to directly reinforce answer tokens rather than improve reasoning. We propose OM-GRPO, a label-free RLVR framework that decouples reward estimation from policy optimization. OM-GRPO masks gradients on the answer span while retaining answer-level rewards through a soft consensus signal, shifting optimization pressure away from answer tokens. We further introduce Contrast-Augmented Reward, which refines reward estimation via low-cost pairwise comparisons over existing trajectories without additional rollouts. Across diverse reasoning benchmarks and three LLM backbones, OM-GRPO outperforms label-free RLVR methods, matches supervised GT-Reward training with stable optimization, and gains 4.24 points over majority voting in Test-Time Training.</abstract>
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%0 Conference Proceedings
%T Don’t Peek at the Answer: Outcome-Masked Group Relative Policy Optimization for Label-Free RLVR
%A Ye, Yongshi
%A Zhang, Liang
%A Chen, Yidong
%A Shi, Xiaodong
%A Fu, Biao
%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 ye-etal-2026-dont
%X Reinforcement Learning with Verifiable Rewards (RLVR) improves LLM reasoning but typically relies on ground-truth (GT) answers, limiting scalability. Voting-based label-free RLVR replaces gold supervision with answer-level consensus from model samples. However, collapse arises when the same answer-level signal is used both to estimate rewards and to drive token-level policy optimization, encouraging the model to directly reinforce answer tokens rather than improve reasoning. We propose OM-GRPO, a label-free RLVR framework that decouples reward estimation from policy optimization. OM-GRPO masks gradients on the answer span while retaining answer-level rewards through a soft consensus signal, shifting optimization pressure away from answer tokens. We further introduce Contrast-Augmented Reward, which refines reward estimation via low-cost pairwise comparisons over existing trajectories without additional rollouts. Across diverse reasoning benchmarks and three LLM backbones, OM-GRPO outperforms label-free RLVR methods, matches supervised GT-Reward training with stable optimization, and gains 4.24 points over majority voting in Test-Time Training.
%U https://aclanthology.org/2026.emnlp-main.2547/
%P 57905-57928
Markdown (Informal)
[Don’t Peek at the Answer: Outcome-Masked Group Relative Policy Optimization for Label-Free RLVR](https://aclanthology.org/2026.emnlp-main.2547/) (Ye et al., EMNLP 2026)
ACL