When to Continue Thinking: Adaptive Thinking Mode Switching for Efficient Reasoning (2025)

Abstract: 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.

## introduction recent advances in large reasoning models (lrms), such as openai-o1 @xcite and @xcite , have greatly improved performance on complex reasoning tasks @xcite . however, when trained with reinforcement learning (rl) @xcite , these models often generate unnecessarily long reasoning chains, causing substantial computational overhead. prior work @xcite @xcite has identified this "overthinking" * corresponding author. let abcdef be a convex equilateral hexagon in which all pairs of opposite sides are parallel. the triangle whose sides are extensions of segments ab, cd, and ef has side lengths 200, 240, and 300. find the side length of the hexagon. phenomenon, where redundant reasoning persists even for simple questions, such as "which is larger, 0.9 or 0.11?", despite efforts such as short-chain datasets and length-based rewards. ideally, lrms should dynamically adjust their reasoning length, using detailed reasoning for complex problems and minimal or no reasoning for simple ones to maximize efficiency. recent work @xcite systematically explored lrm performance in a no-thinking mode, 1 where models generate answers without explicit reasoning chains. these studies show that lrms can still achieve high accuracy under the no-thinking mode, especially as the number of parallel samples increases. our empirical analysis reveals that this is partly because models sometimes implicitly supplement the reasoning process during answer generation, particularly for challenging questions, a phenomenon we refer to as continue-thinking. for example, as illustrated in figure 1 , when the model engages in continue-thinking, it successfully arrives at the correct answer for a challenging problem. in contrast, the model fails to answer correctly. we term this continue-thinking phenomenon as the "internal self-recovery mechanism", indicating that models possess a preliminary ability for difficulty perception and reasoning budget allocation. however, while this self-adaptive behavior is promising, our analysis reveals two major limitations: @xcite models often fail to sufficiently engage in continue-thinking behavior on more difficult questions, resulting in incomplete reasoning and lower accuracy; and (2) they may unnecessarily invoke continue-thinking on simple questions, leading to overthinking and inefficiency. these issues highlight the need for more accurate difficulty perception and more rational allocation of reasoning resources. to address these challenges, we propose the adaptive self-recovery reasoning (asrr) framework, which guides lrms to dynamically adjust reasoning length based on problem difficulty. asrr introduces an accuracy-thresholded reward mechanism: length penalties are applied only when sufficient accuracy is achieved within a group, balancing efficiency and correctness. experiments across various models and benchmarks show that asrr significantly reduces reasoning length while maintaining performance. moreover, asrr enhances the correlation between continue-thinking frequency and problem difficulty, reflecting improved difficulty perception and budget allocation. the main contributions of this paper are as follows: ## methodology in this section, we first present our systematic analysis, which reveals that lrms possess a latent ability to supplement missing reasoning steps during answer generation, a phenomenon we term the "internal self-recovery mechanism." building upon this observation, we then introduce our proposed adaptive self-recovery reasoning framework. ## related work large reasoning models. large reasoning models (lrms) enhance large language models by increasing inference-time computation @xcite rather than simply scaling parameters. chainof-thought (cot) prompting @xcite @xcite , which introduces intermediate reasoning steps, significantly boosts performance on complex tasks. building upon this, recent works further optimize reasoning via reinforcement learning, leading to advanced models such as openai o1 (openai, 2024), @xcite , kimi k1.5 @xcite , and qwq @xcite . trained with answer-based rewards, these models autonomously extend reasoning chains at inference, achieving substantial gains on challenging tasks like advanced mathematics and logical reasoning @xcite . efficient reasoning. despite significant advances in reasoning, lrms often exhibit the "overthinking" problem @xcite @xcite @xcite : for simple questions, they generate unnecessarily long and redundant reasoning chains, leading to inefficiency. prior research has addressed this issue from several perspectives: (a) model optimization: utilizes techniques such as supervised fine-tuning (sft) @xcite @xcite and direct preference optimization (dpo) @xcite @xcite to enable fine-grained control over output length by curating datasets with short reasoning chains. reinforcement learning with length-based rewards @xcite @xcite encourages concise reasoning. while some hybrid models combine reasoning and nonreasoning modules @xcite to balance performance and efficiency. (b) prompt control: use varying prompts to enforce reasoning models to generate concise cot with less unnecessary reasoning steps @xcite @xcite . (c) dynamic reasoning step control: focuses on optimizing the best-of-n sampling, , for example by pruning low-quality samples @xcite or implementing early stopping strategies @xcite @xcite to reduce superfluous computation. unlike previous approaches that rely on explicit control mechanisms or prompt engineering to reduce redundant reasoning, our method enables lrms to adaptively allocate reasoning effort based on task difficulty. by introducing accuracythresholded length rewards, we achieve efficient and adaptive reasoning without sacrificing performance or requiring additional control structures. ## conclusion in this work, we address the trade-off between performance and efficiency in large reasoning models (lrms). by analyzing model behavior under both long-and no-thinking modes, we uncover the "internal self-recovery mechanism" and propose the adaptive self-recovery reasoning (asrr) framework. asrr balances difficulty perception and reasoning budget by suppressing unnecessary reasoning and enabling implicit recovery, using accuracyaware length rewards to adaptively allocate reasoning effort based on problem difficulty. experiments across multiple benchmarks show that asrr reduces reasoning length 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%). these results demonstrate that asrr makes lrms more efficient, adaptive, and safe, paving the way for practical and reliable reasoning systems.