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arXiv · 2604.07922

SAT: Balancing Reasoning Accuracy and Efficiency with Stepwise Adaptive Thinking

Weiyang Huang·Xuefeng Bai·Kehai Chen·Xinyang Chen·Yibin Chen·Weili Guan·Min Zhang
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Abstract

Large Reasoning Models (LRMs) have revolutionized complex problem-solving, yet they exhibit a pervasive "overthinking", generating unnecessarily long reasoning chains. While current solutions improve token efficiency, they often sacrifice fine-grained control or risk disrupting the logical integrity of the reasoning process. To address this, we introduce Stepwise Adaptive Thinking (SAT), a framework that performs step-level, difficulty-aware pruning while preserving the core reasoning structure. SAT formulates reasoning as a Finite-State Machine (FSM) with distinct thinking modes (Slow, Normal, Fast, Skip). It navigates these states dynamically using a lightweight Process Reward Model (PRM), compressing easy steps while preserving depth for hard ones. Experiments across 9 LRMs and 7 benchmarks show that SAT achieves up to 40% reduction in reasoning tokens while generally maintaining or improving accuracy.

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Weiyang Huang, Xuefeng Bai, Kehai Chen, Xinyang Chen, Yibin Chen, Weili Guan, Min Zhang. 2026-04-09. SAT: Balancing Reasoning Accuracy and Efficiency with Stepwise Adaptive Thinking. https://arxiv.org/abs/2604.07922

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