When to Continue Thinking: Adaptive Thinking Mode Switching for
  Efficient Reasoning
Authors: name: Xiaoyun Zhang, name: Jingqing Ruan, name: Xing Ma, name: Yawen Zhu, name: Haodong Zhao, name: Hao Li, name: Jiansong Chen, name: Ke Zeng, name: Xunliang Cai

Large reasoning models (LRMs) achieve remarkable performance via long
reasoning chains, but often incur excessive computational overhead due to
redundant reasoning, especially on simple tasks. In this work, we
systematically quantify the upper bounds of LRMs under both Long-Thinking and
No-Thinking modes, and uncover the phenomenon of "Internal Self-Recovery
Mechanism" where models implicitly supplement reasoning during answer
generation. Building on this insight, we propose Adaptive Self-Recovery
Reasoning (ASRR), a framework that suppresses unnecessary reasoning and enables
implicit recovery. By introducing accuracy-aware length reward regulation, ASRR
adaptively allocates reasoning effort according to problem difficulty,
achieving high efficiency with negligible performance sacrifice. Experiments
across multiple benchmarks and models show that, compared with GRPO, ASRR
reduces reasoning budget by up to 32.5% (1.5B) and 25.7% (7B) with minimal
accuracy loss (1.2% and 0.6% pass@1), and significantly boosts harmless rates
on safety benchmarks (up to +21.7%). Our results highlight the potential of
ASRR for enabling efficient, adaptive, and safer reasoning in LRMs.