@inproceedings{he-etal-2026-smartthinker,
title = "{S}mart{T}hinker: Learning High-Density Reasoning via Step-Level Token Allocation",
author = "He, Xingyang and
Liu, Jie and
Ling, Xiao",
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.640/",
doi = "10.18653/2026.findings-emnlp.640",
pages = "13409--13428",
ISBN = "979-8-89176-462-0",
abstract = "Efficient reasoning is critical for large reasoning models (LRMs) deployed in agent systems with strict context constraints. Existing methods predominantly rely on global length penalties that overlook step-level structure, inadvertently compressing critical derivations while retaining low-value detours. Hence, we argue for a principled, step-aware alternative that preserves critical steps and compresses low-value detours. However, it raises three fundamental challenges: the difficulty of obtaining step-level supervision, the need for reward signals that induce differentiated length pressure, and the requirement to balance training stability with exploration on complex problems. We propose SmartThinker, a step-level token allocation framework that learns to selectively preserve or compress individual reasoning steps. At its core is Step-Level Length Control Policy Optimization (SCPO), which provides label-free feedback by online estimation of each step{'}s contribution from the change in correct-answer likelihood after removing it. We further introduce reasoning density, defined as contribution per token, to relax length pressure on high-value steps while intensifying it on low-density ones. Finally, SCPO stabilizes training through length-bias-corrected advantages and difficulty-adaptive clipping, achieving superior accuracy-efficiency trade-offs across nine in-domain and out-of-domain benchmarks."
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<abstract>Efficient reasoning is critical for large reasoning models (LRMs) deployed in agent systems with strict context constraints. Existing methods predominantly rely on global length penalties that overlook step-level structure, inadvertently compressing critical derivations while retaining low-value detours. Hence, we argue for a principled, step-aware alternative that preserves critical steps and compresses low-value detours. However, it raises three fundamental challenges: the difficulty of obtaining step-level supervision, the need for reward signals that induce differentiated length pressure, and the requirement to balance training stability with exploration on complex problems. We propose SmartThinker, a step-level token allocation framework that learns to selectively preserve or compress individual reasoning steps. At its core is Step-Level Length Control Policy Optimization (SCPO), which provides label-free feedback by online estimation of each step’s contribution from the change in correct-answer likelihood after removing it. We further introduce reasoning density, defined as contribution per token, to relax length pressure on high-value steps while intensifying it on low-density ones. Finally, SCPO stabilizes training through length-bias-corrected advantages and difficulty-adaptive clipping, achieving superior accuracy-efficiency trade-offs across nine in-domain and out-of-domain benchmarks.</abstract>
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%0 Conference Proceedings
%T SmartThinker: Learning High-Density Reasoning via Step-Level Token Allocation
%A He, Xingyang
%A Liu, Jie
%A Ling, Xiao
%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 he-etal-2026-smartthinker
%X Efficient reasoning is critical for large reasoning models (LRMs) deployed in agent systems with strict context constraints. Existing methods predominantly rely on global length penalties that overlook step-level structure, inadvertently compressing critical derivations while retaining low-value detours. Hence, we argue for a principled, step-aware alternative that preserves critical steps and compresses low-value detours. However, it raises three fundamental challenges: the difficulty of obtaining step-level supervision, the need for reward signals that induce differentiated length pressure, and the requirement to balance training stability with exploration on complex problems. We propose SmartThinker, a step-level token allocation framework that learns to selectively preserve or compress individual reasoning steps. At its core is Step-Level Length Control Policy Optimization (SCPO), which provides label-free feedback by online estimation of each step’s contribution from the change in correct-answer likelihood after removing it. We further introduce reasoning density, defined as contribution per token, to relax length pressure on high-value steps while intensifying it on low-density ones. Finally, SCPO stabilizes training through length-bias-corrected advantages and difficulty-adaptive clipping, achieving superior accuracy-efficiency trade-offs across nine in-domain and out-of-domain benchmarks.
%R 10.18653/2026.findings-emnlp.640
%U https://aclanthology.org/2026.findings-emnlp.640/
%U https://doi.org/10.18653/2026.findings-emnlp.640
%P 13409-13428
Markdown (Informal)
[SmartThinker: Learning High-Density Reasoning via Step-Level Token Allocation](https://aclanthology.org/2026.findings-emnlp.640/) (He et al., Findings 2026)
ACL