Does Your Reasoning Model Implicitly Know When to Stop Thinking?
Recent advancements in large reasoning models (LRMs) have greatly improved their capabilities on complex reasoning tasks through Long Chains of Thought (CoTs). However, this approach often results in substantial redundancy, impairing computational efficiency and causing significant delays in real-time applications. Recent studies show that longer reasoning chains are frequently uncorrelated with correctness and can even be detrimental to accuracy. In a further in-depth analysis of this phenomenon, we surprisingly uncover and empirically verify that LRMs implicitly know the appropriate time to stop thinking, while this capability is obscured by current sampling paradigms. Motivated by this, we introduce SAGE (Self-Aware Guided Efficient Reasoning), a novel sampling paradigm that unleashes this efficient reasoning potential. Furthermore, integrating SAGE as mixed sampling into group-based reinforcement learning (SAGE-RL) enables SAGE-RL to effectively incorporate SAGE-discovered efficient reasoning patterns into standard pass@1 inference, markedly enhancing both the reasoning accuracy and efficiency of LRMs across multiple challenging mathematical benchmarks.
699bcb43f723198bd53a633e
Zixuan Huang
claimed_verified
2026-02-23T09:42:40.470Z
6593d2329e16fa7510e0876a
/avatars/c200676dd9dc1db8a3e27388251aea49.svg
hzx
hzxllll
user
hzxllll
699bcb43f723198bd53a633f
Xin Xia
699bcb43f723198bd53a6340
Yuxi Ren
699bcb43f723198bd53a6341
Jianbin Zheng
699bcb43f723198bd53a6342
Xuanda Wang
699bcb43f723198bd53a6343
Zhixia Zhang
699bcb43f723198bd53a6344
Hongyan Xie
699bcb43f723198bd53a6345
Songshi Liang
699bcb43f723198bd53a6346
Zehao Chen
699bcb43f723198bd53a6347
Xuefeng Xiao
699bcb43f723198bd53a6348
Fuzhen Zhuang
699bcb43f723198bd53a6349
Jianxin Li
699bcb43f723198bd53a634a
Yikun Ban
claimed_verified
2026-02-23T09:42:42.815Z
68345345f4bbf856e2d708e2
https://cdn-avatars.huggingface.co/v1/production/uploads/68345345f4bbf856e2d708e2/L5H2HNCuWje3ti2tNbC5p.jpeg
Yikun Ban
Yikunb
user
Yikunb
699bcb43f723198bd53a634b
Deqing Wang
Does Your Reasoning Model Implicitly Know When to Stop Thinking?
Recent advancements in large reasoning models (LRMs) have greatly improved their capabilities on complex reasoning tasks through Long Chains of Thought (CoTs). However, this approach often results in substantial redundancy, impairing computational efficiency and causing significant delays in real-time applications. Recent studies show that longer reasoning chains are frequently uncorrelated with correctness and can even be detrimental to accuracy. In a further in-depth analysis of this phenomenon, we surprisingly uncover and empirically verify that LRMs implicitly know the appropriate time to stop thinking, while this capability is obscured by current sampling paradigms. Motivated by this, we introduce SAGE (Self-Aware Guided Efficient Reasoning), a novel sampling paradigm that unleashes this efficient reasoning potential. Furthermore, integrating SAGE as mixed sampling into group-based reinforcement learning (SAGE-RL) enables SAGE-RL to effectively incorporate SAGE-discovered efficient reasoning patterns into standard pass@1 inference, markedly enhancing both the reasoning accuracy and efficiency of LRMs across multiple challenging mathematical benchmarks.
Large reasoning models can implicitly determine optimal stopping points for thinking, which SAGE-RL enhances by incorporating efficient reasoning patterns into pass@1 inference for improved accuracy and efficiency.
large reasoning models chains of thought sampling paradigms self-aware guided efficient reasoning group-based reinforcement learning pass@1 inference mathematical benchmarks