@inproceedings{wu-etal-2026-beyond,
title = "Beyond Token Length: Step Pruner for Efficient and Accurate Reasoning in Large Language Models",
author = "Wu, Canhui and
Cao, Qiong and
Li, Chang and
Wang, Zhenfang and
Xue, Chao and
Fan, Yuwei and
Xi, Wei and
He, Xiaodong",
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.94/",
pages = "1953--1974",
ISBN = "979-8-89176-395-1",
abstract = "Large Reasoning Models (LRMs) demonstrate strong performance on complex tasks but often suffer from excessive verbosity, known as ``overthinking.'' Existing solutions via reinforcement learning (RL) typically penalize generated tokens to promote conciseness. However, these methods encounter two challenges: responses with fewer tokens do not always correspond to fewer reasoning steps, and models may develop hacking behavior in later stages of training by discarding reasoning steps to minimize token usage. In this work, we introduce \textbf{Step Pruner (SP)}, an RL framework that steers LRMs toward more efficient reasoning by favoring compact reasoning steps. Our step-aware reward function prioritizes correctness while imposing penalties for redundant steps, and withholds rewards for incorrect responses to prevent the reinforcement of erroneous reasoning. Moreover, we propose a dynamic stopping mechanism to prevent hacking behavior caused by step merging. Extensive experiments across four reasoning benchmarks demonstrate that SP achieves state-of-the-art accuracy while significantly reducing response length. For instance, on AIME24, SP reduces token usage by \textbf{69.7{\%}}."
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<abstract>Large Reasoning Models (LRMs) demonstrate strong performance on complex tasks but often suffer from excessive verbosity, known as “overthinking.” Existing solutions via reinforcement learning (RL) typically penalize generated tokens to promote conciseness. However, these methods encounter two challenges: responses with fewer tokens do not always correspond to fewer reasoning steps, and models may develop hacking behavior in later stages of training by discarding reasoning steps to minimize token usage. In this work, we introduce Step Pruner (SP), an RL framework that steers LRMs toward more efficient reasoning by favoring compact reasoning steps. Our step-aware reward function prioritizes correctness while imposing penalties for redundant steps, and withholds rewards for incorrect responses to prevent the reinforcement of erroneous reasoning. Moreover, we propose a dynamic stopping mechanism to prevent hacking behavior caused by step merging. Extensive experiments across four reasoning benchmarks demonstrate that SP achieves state-of-the-art accuracy while significantly reducing response length. For instance, on AIME24, SP reduces token usage by 69.7%.</abstract>
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%0 Conference Proceedings
%T Beyond Token Length: Step Pruner for Efficient and Accurate Reasoning in Large Language Models
%A Wu, Canhui
%A Cao, Qiong
%A Li, Chang
%A Wang, Zhenfang
%A Xue, Chao
%A Fan, Yuwei
%A Xi, Wei
%A He, Xiaodong
%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 wu-etal-2026-beyond
%X Large Reasoning Models (LRMs) demonstrate strong performance on complex tasks but often suffer from excessive verbosity, known as “overthinking.” Existing solutions via reinforcement learning (RL) typically penalize generated tokens to promote conciseness. However, these methods encounter two challenges: responses with fewer tokens do not always correspond to fewer reasoning steps, and models may develop hacking behavior in later stages of training by discarding reasoning steps to minimize token usage. In this work, we introduce Step Pruner (SP), an RL framework that steers LRMs toward more efficient reasoning by favoring compact reasoning steps. Our step-aware reward function prioritizes correctness while imposing penalties for redundant steps, and withholds rewards for incorrect responses to prevent the reinforcement of erroneous reasoning. Moreover, we propose a dynamic stopping mechanism to prevent hacking behavior caused by step merging. Extensive experiments across four reasoning benchmarks demonstrate that SP achieves state-of-the-art accuracy while significantly reducing response length. For instance, on AIME24, SP reduces token usage by 69.7%.
%U https://aclanthology.org/2026.findings-acl.94/
%P 1953-1974
Markdown (Informal)
[Beyond Token Length: Step Pruner for Efficient and Accurate Reasoning in Large Language Models](https://aclanthology.org/2026.findings-acl.94/) (Wu et al., Findings 2026)
ACL
- Canhui Wu, Qiong Cao, Chang Li, Zhenfang Wang, Chao Xue, Yuwei Fan, Wei Xi, and Xiaodong He. 2026. Beyond Token Length: Step Pruner for Efficient and Accurate Reasoning in Large Language Models. In Findings of the Association for Computational Linguistics: ACL 2026, pages 1953–1974, San Diego, California, United States. Association for Computational Linguistics.