@inproceedings{wei-etal-2026-taming,
title = "Taming the Thinker: Conditional Entropy Shaping for Adaptive {LLM} Reasoning",
author = "Wei, Shuyu and
Sun, Jian and
Qiu, Delai and
Wang, Yining and
Liu, Shengping and
Liang, Jerry and
Fu, Ying and
Huang, Wei and
Sang, Jitao",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1604/",
pages = "34167--34180",
ISBN = "979-8-89176-462-0",
abstract = "Entropy-based deep reasoning has emerged as a promising direction for improving the reasoning capabilities of Large Language Models (LLMs), but existing methods often either increase response length indiscriminately or shorten responses at the cost of accuracy. To better balance this trade-off, we introduce Conditional Entropy Shaping (CES), a framework that dynamically controls token-level response entropy, enabling LLMs to produce concise solutions on simple problems while encouraging deeper exploration on hard ones. Built on DAPO, CES uses token-level entropy as an uncertainty signal and applies a conditional bidirectional policy: it penalizes high-entropy ``forking point'' tokens on correct reasoning paths to improve conciseness, and rewards them on incorrect paths to encourage exploration and error correction. We implement CES on DeepSeek-R1-Distill-Qwen-7B and evaluate it on 12 mathematical benchmarks. CES consistently improves average accuracy while reducing response length relative to DAPO, and supplementary experiments show similar trends on a smaller 1.5B backbone and on out-of-domain benchmarks."
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<abstract>Entropy-based deep reasoning has emerged as a promising direction for improving the reasoning capabilities of Large Language Models (LLMs), but existing methods often either increase response length indiscriminately or shorten responses at the cost of accuracy. To better balance this trade-off, we introduce Conditional Entropy Shaping (CES), a framework that dynamically controls token-level response entropy, enabling LLMs to produce concise solutions on simple problems while encouraging deeper exploration on hard ones. Built on DAPO, CES uses token-level entropy as an uncertainty signal and applies a conditional bidirectional policy: it penalizes high-entropy “forking point” tokens on correct reasoning paths to improve conciseness, and rewards them on incorrect paths to encourage exploration and error correction. We implement CES on DeepSeek-R1-Distill-Qwen-7B and evaluate it on 12 mathematical benchmarks. CES consistently improves average accuracy while reducing response length relative to DAPO, and supplementary experiments show similar trends on a smaller 1.5B backbone and on out-of-domain benchmarks.</abstract>
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%0 Conference Proceedings
%T Taming the Thinker: Conditional Entropy Shaping for Adaptive LLM Reasoning
%A Wei, Shuyu
%A Sun, Jian
%A Qiu, Delai
%A Wang, Yining
%A Liu, Shengping
%A Liang, Jerry
%A Fu, Ying
%A Huang, Wei
%A Sang, Jitao
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wei-etal-2026-taming
%X Entropy-based deep reasoning has emerged as a promising direction for improving the reasoning capabilities of Large Language Models (LLMs), but existing methods often either increase response length indiscriminately or shorten responses at the cost of accuracy. To better balance this trade-off, we introduce Conditional Entropy Shaping (CES), a framework that dynamically controls token-level response entropy, enabling LLMs to produce concise solutions on simple problems while encouraging deeper exploration on hard ones. Built on DAPO, CES uses token-level entropy as an uncertainty signal and applies a conditional bidirectional policy: it penalizes high-entropy “forking point” tokens on correct reasoning paths to improve conciseness, and rewards them on incorrect paths to encourage exploration and error correction. We implement CES on DeepSeek-R1-Distill-Qwen-7B and evaluate it on 12 mathematical benchmarks. CES consistently improves average accuracy while reducing response length relative to DAPO, and supplementary experiments show similar trends on a smaller 1.5B backbone and on out-of-domain benchmarks.
%U https://aclanthology.org/2026.findings-emnlp.1604/
%P 34167-34180
Markdown (Informal)
[Taming the Thinker: Conditional Entropy Shaping for Adaptive LLM Reasoning](https://aclanthology.org/2026.findings-emnlp.1604/) (Wei et al., Findings 2026)
ACL
- Shuyu Wei, Jian Sun, Delai Qiu, Yining Wang, Shengping Liu, Jerry Liang, Ying Fu, Wei Huang, and Jitao Sang. 2026. Taming the Thinker: Conditional Entropy Shaping for Adaptive LLM Reasoning. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 34167–34180, Budapest, Hungary. Association for Computational Linguistics.