T hink S witcher: When to Think Hard, When to Think Fast (2025)

Abstract: Large reasoning models (LRMs) excel at solving complex tasks by leveraging long chain-of-thought (CoT) reasoning. However, this often leads to overthinking on simple tasks, resulting in unnecessary computational overhead. We observe that LRMs inherently possess the capability for efficient short CoT reasoning, which can be reliably elicited through prompt design. To leverage this capability, we propose ThinkSwitcher, a framework that enables a single LRM to dynamically switch between short and long CoT modes based on task complexity. ThinkSwitcher introduces a lightweight switching module trained with supervision signals derived from the relative performance of each reasoning mode across tasks. Experiments on multiple reasoning benchmarks show that ThinkSwitcher reduces computational cost by 20-30% while maintaining high accuracy on complex tasks. This demonstrates the effectiveness of ThinkSwitcher as a scalable and efficient solution for unified LRM deployment.

## introduction large reasoning models (lrms) @xcite @xcite have demonstrated impressive capabilities in solving complex tasks. they achieve this through long chain-of-thought (cot) processes, which involve behaviors such as exploration, self-reflection, and verification @xcite @xcite . however, this strength can become a drawback: lrms tend to overthink @xcite @xcite even the simplest problems (e.g., 2+3=?), which unnecessarily invokes elaborate reasoning for tasks that require minimal effort. this inefficiency becomes especially problematic in high-throughput applications @xcite . to mitigate this problem, many systems deploy two separate models: one tailored for complex reasoning and another for simpler tasks. while effective, this dual-model setup incurs additional computational and memory costs. this raises a fundamental question: can a single model achieve both robust reasoning capabilities and high efficiency? inspired by the adaptive nature of human cognition-such as the system 1 and system 2 framework @xcite @xcite -we investigate enabling a single powerful reasoning model to operate in two distinct modes: its native long cot mode for complex problems and an efficient short cot mode for simpler tasks. recent works such as gemini-2.5-pro (google, 2025), qwen3 @xcite , and llama-nemotron @xcite have also explored dual-mode systems. however, these ap-proaches often lack public implementation details (google, 2025) or depend on post-training with curated long and short cot data @xcite , and typically require manual mode selection based on user inputs. in contrast, we propose a lightweight and adaptive alternative: a switcher module that automatically selects the appropriate reasoning mode based on task complexity, without backbone changes or large-scale training. our investigation reveals a key insight, echoed by @xcite : advanced lrms already possess a latent capability for concise and effective short cot reasoning. figure 1 illustrates a case where long cot reasoning results in excessive token consumption, while short cot achieves an accurate answer with substantially fewer tokens. notably, we find that the short cot capability can be reliably activated by appending an empty thinking block (e.g., <@xmath0. this simple promptbased intervention requires no changes to the model itself. beyond this empirical finding, we offer a theoretical explanation of this latent behavior and its supporting mechanisms, detailed in appendix c. building on these observations, we propose the thinkswitcher framework, which enables a single lrm to adaptively switch between long and short cot modes. to support this capability, a lightweight switcher module is employed to predict the reasoning mode likely to yield optimal performance for a given query. the switcher is trained using self-supervised signals derived from the backbone model's own performance when executing both reasoning modes. this eliminates the need for external annotation or extensive post-training. our experiments show that thinkswitcher noticeably reduces average token usage across various benchmarks while maintaining high accuracy on complex reasoning tasks by retaining long cot where necessary. for example, on simpler datasets such as gsm8k @xcite , it reduces inference tokens by around 30% with a performance loss of less than 1%. on more challenging datasets like aime (maa, 2025), thinkswitcher achieves token reductions of 38%, with only approximately a 2% decline in performance. overall, it consistently lowers computational costs by 20-30% across benchmarks while retaining highly competitive accuracy. these results validate our approach to unifying strong reasoning capabilities with efficient resource usage in a single model deployment. ## related work large reasoning models large reasoning models (lrms), such as openai-o1 @xcite , @xcite , and qwq (qwenteam, 2025), are designed to emulate system-2 reasoning @xcite . these lrms have demonstrated state-of-the-art performance on challenging tasks in mathematics @xcite and coding @xcite . these models are typically trained via reinforcement learning (rl) algorithms @xcite to elicit long chain-of-thought (cot) reasoning. however, their tendency to overthink @xcite even on simple questions results in substantial computational inefficiencies and restricts their practicality in highthroughput scenarios. several dual-system models have been introduced to mitigate this, including claude-3.7-sonnet @xcite , gemini-2.5-pro (google, 2025), qwen3 @xcite , and llama-nemetron @xcite . these models offer both long and short cot modes, allowing users to choose between deep reasoning and quick answers depending on themselves. among these, approaches from @xcite and @xcite implement this dual-mode capability through post-training on carefully curated mixtures of long and short cot datasets. nonetheless, most of these models rely on manual mode selection, and lack the ability to automatically adapt the reasoning depth based on the input query. in contrast, we propose a lightweight mechanism that allows a single model to dynamically switch between short and long cot modes. this enables both efficiency and strong reasoning without modifying weights or requiring post-training. efficient reasoning extensive research has focused on reducing inference overhead and improving reasoning efficiency @xcite . among these methods, kimi-k1.5 @xcite and o1-pruner @xcite introduce length-controlled reward functions in reinforcement learning to reduce cot reasoning length. alternatively, methods such as dast @xcite , c3ot @xcite , and tokenskip @xcite train lrms to generate compact cots by constructing datasets with varying reasoning lengths and applying post-training techniques like sft, dpo @xcite , or simpo @xcite . nevertheless, both strategies necessitate additional lrm training, which introduces significant computational costs. a distinct direction involves prompt-guided strategies, such as cod @xcite and ccot @xcite , which guide models to directly produce concise reasoning without fine-tuning. however, these methods lack the ability to dynamically adapt reasoning depth to question complexity, leading to performance degradation on challenging tasks @xcite . another line of work on llm-routing, such as routellm @xcite , employs a router to distribute user questions across specialized llms. this approach assigns questions to the most suitable model to reduce average inference costs. while effective, it requires the simultaneous deployment of multiple llms. in contrast, our method achieves efficient reasoning with a single lrm, avoiding both significant fine-tuning costs and multi-model deployment complexities. this orthogonal approach dynamically adjusts reasoning depth and strikes an effective balance between deployment overhead and system performance. ## observations large reasoning models (lrms) such as @xcite , sky-t1-32b @xcite , and qwen3 @xcite commonly employ structured generation formats to tackle complex reasoning tasks. a notable characteristic of these models is the use of special tokens, @xmath1, which explicitly separate the model's intermediate reasoning process from the final answer. the content after @xmath2. ## methodology to strike an effective balance between reasoning performance and computational efficiency, we propose thinkswitcher which dynamically switches between short and long chain-of-thought reasoning modes based on the input. figure 3 illustrates the overall workflow of this framework. ## analyses to gain a deeper understanding of the behavior and effectiveness of thinkswitcher, we conduct in-depth analyses. these include evaluating cross domain generalization (sec 6.1), examining decision dynamics (sec 6.2), quantifying the impact of different training objectives (sec 6.3), evaluating key design components (sec 6.4), and assessing the computational efficiency of the switching mechanism (sec 6.5). unless otherwise specified, experiments in this section use the deepseek-r1-distill-qwen-7b model, selected for its favorable trade-off between efficiency and generality. ## conclusion while large reasoning models (lrms) excel at complex tasks, they often default to unnecessarily long chain-of-thought (cot) reasoning even for simple problems and lead to significant computational overhead. we introduced thinkswitcher, a lightweight and adaptive framework that addresses this overthinking tendency by dynamically selecting between short and long cot modes based on task complexity. thinkswitcher harnesses the model's latent ability for concise reasoning-elicited via prompt design-and integrates a self-supervised switcher module to automate mode selection without modifying the backbone architecture or requiring additional training. experiments across diverse benchmarks show that thinkswitcher reduces computational cost by 20-30% while preserving strong performance on complex tasks. these findings highlight adaptive reasoning control as a scalable and effective approach to mitigating overthinking in lrms and supporting efficient unified deployment.