让大模型根据任务难易自动选择快慢推理，省时又精准。
ThinkSwitcher: When to Think Hard, When to Think Fast
- 通过提示工程触发模型的短链推理能力，动态切换思考模式。
- 在多个基准上降低20%-30%计算开销，复杂任务准确率不变。
- 适合需要高效部署大模型的场景，如实时应用或资源受限环境。
大型推理模型（LRMs）擅长通过长链式思维（CoT）解决复杂任务，但对简单任务常过度思考，造成不必要的计算开销。我们发现LRMs本身具备高效短链推理能力，可通过提示设计可靠激发。为此提出ThinkSwitcher框架，使单一LRM能根据任务复杂度动态切换短/长链推理模式。该框架引入轻量级切换模块，利用各推理模式在任务上的相对表现作为监督信号进行训练。在多个推理基准上的实验表明，ThinkSwitcher将计算成本降低20%-30%，同时保持复杂任务的高准确率。这证明ThinkSwitcher是统一部署大型推理模型的一种可扩展、高效的解决方案。
原文摘要 · Abstract (English)
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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