DAST: Difficulty-Adaptive Slow-Thinking for Large Reasoning Models

Understand

Recent advancements in slow thinking reasoning models have shown exceptional performance in complex reasoning tasks.

• However, these models often exhibit overthinking (generating redundant reasoning steps for simple problems), leading to excessive computational resource usage.
• While current mitigation strategies uniformly reduce reasoning tokens, they risk degrading performance on challenging tasks that require extended reasoning.
• This paper introduces Difficulty-Adaptive Slow Thinking (DAST), a novel framework that enables models to autonomously adjust the length of Chain-of-Thought (CoT) based on problem difficulty.