AutoL2S: Auto Long-Short Reasoning for Efficient Large Language Models

Understand

Reasoning-capable large language models (LLMs) achieve strong performance on complex tasks but often exhibit overthinking after distillation, generating unnecessarily long chain-of-thought (CoT) reasoning even for simple inputs and incurring high inference cost.

• However, naively shortening reasoning length can degrade reasoning accuracy, as concise reasoning may be insufficient for certain inputs and lacks explicit supervision.
• We propose Auto Long-Short Reasoning (AutoL2S), a distillation framework that empowers non-reasoning LLMs to think thoroughly but only when necessary.
• AutoL2S first learns a lightweight switching token with verified long-short CoTs to enable instance-wise long-short reasoning selection.