ThinkSwitcher: When to Think Hard, When to Think Fast Article Swipe

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Guosheng Liang , Liang Zhong , Ziyi Yang , Xiaojun Quan ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2505.14183

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.

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2505.14183
PDF: https://arxiv.org/pdf/2505.14183
OA Status: green
OpenAlex ID: https://openalex.org/W4415022844

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OpenAlex ID: https://openalex.org/W4415022844

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DOI: https://doi.org/10.48550/arxiv.2505.14183

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Title: ThinkSwitcher: When to Think Hard, When to Think Fast

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-05-20

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Authors: Guosheng Liang, Liang Zhong, Ziyi Yang, Xiaojun Quan

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Landing page: https://arxiv.org/abs/2505.14183

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PDF URL: https://arxiv.org/pdf/2505.14183

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Open access: Yes

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OA status: green

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OA URL: https://arxiv.org/pdf/2505.14183

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Cited by: 0

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