@inproceedings{yuan-etal-2026-shorten,
title = "Shorten After You{'}re Right: Lazy Length Penalties for Reasoning {RL}",
author = "Yuan, Danlong and
Xie, Tian and
Huang, Shaohan and
Zhang, Huishuai and
Gong, Zhuocheng and
Luo, Chong and
Wei, Furu and
Zhao, Dongyan",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.626/",
pages = "12864--12877",
ISBN = "979-8-89176-395-1",
abstract = "Long-reasoning models achieve strong accuracy on complex reasoning tasks, but their extended reasoning trajectories incur substantial memory and latency costs. Several existing shortening methods rely on additional supervision or multi-stage post-training, which primarily reduces inference length and does not reduce the rollout tokens during on-policy reinforcement learning (RL). We instead target on-policy response shortening, aiming to improve both inference efficiency and RL training throughput. However, because on-policy RL couples optimization with exploration, naively penalizing length can destabilize training and suppress exploration. To impose length pressure safely, we propose a lazy length penalty integrated into the rule-based RL pipeline: it activates only on correct trajectories, only after training accuracy enters a stably improving regime, and only when responses exceed a tolerance band beyond the minimal correct length. Across four settings, our method significantly reduces response length without extra training stages while maintaining or improving performance. In a logic reasoning setting, we achieve a 40{\%} reduction in step-averaged response length alongside a 14-point gain in performance. For math problems, we reduce step-averaged response length by 33{\%} while preserving performance."
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<abstract>Long-reasoning models achieve strong accuracy on complex reasoning tasks, but their extended reasoning trajectories incur substantial memory and latency costs. Several existing shortening methods rely on additional supervision or multi-stage post-training, which primarily reduces inference length and does not reduce the rollout tokens during on-policy reinforcement learning (RL). We instead target on-policy response shortening, aiming to improve both inference efficiency and RL training throughput. However, because on-policy RL couples optimization with exploration, naively penalizing length can destabilize training and suppress exploration. To impose length pressure safely, we propose a lazy length penalty integrated into the rule-based RL pipeline: it activates only on correct trajectories, only after training accuracy enters a stably improving regime, and only when responses exceed a tolerance band beyond the minimal correct length. Across four settings, our method significantly reduces response length without extra training stages while maintaining or improving performance. In a logic reasoning setting, we achieve a 40% reduction in step-averaged response length alongside a 14-point gain in performance. For math problems, we reduce step-averaged response length by 33% while preserving performance.</abstract>
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%0 Conference Proceedings
%T Shorten After You’re Right: Lazy Length Penalties for Reasoning RL
%A Yuan, Danlong
%A Xie, Tian
%A Huang, Shaohan
%A Zhang, Huishuai
%A Gong, Zhuocheng
%A Luo, Chong
%A Wei, Furu
%A Zhao, Dongyan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F yuan-etal-2026-shorten
%X Long-reasoning models achieve strong accuracy on complex reasoning tasks, but their extended reasoning trajectories incur substantial memory and latency costs. Several existing shortening methods rely on additional supervision or multi-stage post-training, which primarily reduces inference length and does not reduce the rollout tokens during on-policy reinforcement learning (RL). We instead target on-policy response shortening, aiming to improve both inference efficiency and RL training throughput. However, because on-policy RL couples optimization with exploration, naively penalizing length can destabilize training and suppress exploration. To impose length pressure safely, we propose a lazy length penalty integrated into the rule-based RL pipeline: it activates only on correct trajectories, only after training accuracy enters a stably improving regime, and only when responses exceed a tolerance band beyond the minimal correct length. Across four settings, our method significantly reduces response length without extra training stages while maintaining or improving performance. In a logic reasoning setting, we achieve a 40% reduction in step-averaged response length alongside a 14-point gain in performance. For math problems, we reduce step-averaged response length by 33% while preserving performance.
%U https://aclanthology.org/2026.findings-acl.626/
%P 12864-12877
Markdown (Informal)
[Shorten After You’re Right: Lazy Length Penalties for Reasoning RL](https://aclanthology.org/2026.findings-acl.626/) (Yuan et al., Findings 2026)
ACL
- Danlong Yuan, Tian Xie, Shaohan Huang, Huishuai Zhang, Zhuocheng Gong, Chong Luo, Furu Wei, and Dongyan Zhao. 2026. Shorten After You’re Right: Lazy Length Penalties for Reasoning RL. In Findings of the Association for Computational Linguistics: ACL 2026, pages 12864–12877, San Diego, California, United States. Association for Computational Linguistics.