SAT: Balancing Reasoning Accuracy and Efficiency with Stepwise Adaptive Thinking
Weiyang Huang Harbin Institute of Technology ,(Shenzhen), Xuefeng Bai, Kehai Chen Harbin Institute of Technology (Shenzhen), Xinyang Chen Harbin Institute of Technology, Shenzhen, Yibin Chen, Weili Guan, Min Zhang Harbin Institute of Technology, Shenzhen and Harbin Institute of Technology
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. Code is available at https://github.com/byxw13/SAT_Code.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表