@inproceedings{xu-etal-2026-adapthink,
title = "{A}dap{T}hink: Adaptive Thinking Preferences for Reasoning Language Models",
author = "Xu, Wenyue and
Wan, Xu and
Wang, Wei and
Huang, Wenqi and
Yin, Wotao and
Zhao, Shengjie and
Sun, Mingyang",
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.477/",
pages = "9808--9825",
ISBN = "979-8-89176-395-1",
abstract = "The slow thinking paradigm has been widely validated to enhance the reasoning capabilities of Large Language Models (LLMs), but it introduces notable reasoning inefficiencies: models often overthink simple tasks while prematurely shifting their reasoning paths when addressing complex problems. To address this, we propose AdapThink, a simple yet efficient framework for adaptive reasoning preference control. Unlike methods imposing uniform length constraints, AdapThink dynamically adjusts reflection preferences based on group-level distributional statistics of reasoning length and reflection intensity. We further introduce a dispersion-based diversity sampling mechanism that maximizes the geometric spread of reasoning patterns, accelerating learning through exposure to diverse problem-solving strategies. Across mathematical reasoning and code generation benchmarks, AdapThink reduces average response length by 17.1{\%}-21.4{\%} while improving performance by 6.12-6.59 points under 32K token budgets, demonstrating superior efficiency and robustness against reward hacking compared to strong baselines."
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<abstract>The slow thinking paradigm has been widely validated to enhance the reasoning capabilities of Large Language Models (LLMs), but it introduces notable reasoning inefficiencies: models often overthink simple tasks while prematurely shifting their reasoning paths when addressing complex problems. To address this, we propose AdapThink, a simple yet efficient framework for adaptive reasoning preference control. Unlike methods imposing uniform length constraints, AdapThink dynamically adjusts reflection preferences based on group-level distributional statistics of reasoning length and reflection intensity. We further introduce a dispersion-based diversity sampling mechanism that maximizes the geometric spread of reasoning patterns, accelerating learning through exposure to diverse problem-solving strategies. Across mathematical reasoning and code generation benchmarks, AdapThink reduces average response length by 17.1%-21.4% while improving performance by 6.12-6.59 points under 32K token budgets, demonstrating superior efficiency and robustness against reward hacking compared to strong baselines.</abstract>
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%0 Conference Proceedings
%T AdapThink: Adaptive Thinking Preferences for Reasoning Language Models
%A Xu, Wenyue
%A Wan, Xu
%A Wang, Wei
%A Huang, Wenqi
%A Yin, Wotao
%A Zhao, Shengjie
%A Sun, Mingyang
%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 xu-etal-2026-adapthink
%X The slow thinking paradigm has been widely validated to enhance the reasoning capabilities of Large Language Models (LLMs), but it introduces notable reasoning inefficiencies: models often overthink simple tasks while prematurely shifting their reasoning paths when addressing complex problems. To address this, we propose AdapThink, a simple yet efficient framework for adaptive reasoning preference control. Unlike methods imposing uniform length constraints, AdapThink dynamically adjusts reflection preferences based on group-level distributional statistics of reasoning length and reflection intensity. We further introduce a dispersion-based diversity sampling mechanism that maximizes the geometric spread of reasoning patterns, accelerating learning through exposure to diverse problem-solving strategies. Across mathematical reasoning and code generation benchmarks, AdapThink reduces average response length by 17.1%-21.4% while improving performance by 6.12-6.59 points under 32K token budgets, demonstrating superior efficiency and robustness against reward hacking compared to strong baselines.
%U https://aclanthology.org/2026.findings-acl.477/
%P 9808-9825
Markdown (Informal)
[AdapThink: Adaptive Thinking Preferences for Reasoning Language Models](https://aclanthology.org/2026.findings-acl.477/) (Xu et al., Findings 2026)
ACL
- Wenyue Xu, Xu Wan, Wei Wang, Wenqi Huang, Wotao Yin, Shengjie Zhao, and Mingyang Sun. 2026. AdapThink: Adaptive Thinking Preferences for Reasoning Language Models. In Findings of the Association for Computational Linguistics: ACL 2026, pages 9808–9825, San Diego, California, United States. Association for Computational Linguistics.