ThinkSwitcher: When to Think Hard, When to Think Fast
This addresses efficiency issues for users of large reasoning models, though it is incremental as it builds on existing chain-of-thought methods.
The paper tackles the problem of large reasoning models overthinking on simple tasks, leading to unnecessary computational overhead, by proposing ThinkSwitcher, a framework that dynamically switches between short and long chain-of-thought reasoning modes based on task complexity, reducing computational cost by 20-30% while maintaining high accuracy on complex tasks.
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.