SOURCE-LINKED INTELLIGENCE
When2Think: Learning Difficulty-Aware Length Control for Efficient Hybrid Reasoning Models
Large Reasoning Models (LRMs) achieve strong performance on complex tasks but exhibit systematic inefficiency: they often overthink easy problems and underthink hard ones. Existing approaches based on uniform length penalties or rigid routing incur an efficiency tax, trading reduced computation on easy instances for accuracy loss on hard instances. We formulate efficient reasoning as an instance-adaptive computation allocation problem and propose When2Think, a post-training framework for hybrid reasoning that dynamically allocates computation based on problem difficulty. Our method introduces
Read original source ↗ Open in workspace
- recordType
- paper
- region
- Global
Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-17T04:15:27.000Z
- arXiv · Artificial Intelligence · 2026-09-17T04:15:27.000Z
First collected: 2026-09-19T20:26:32.566Z. This is not the publication date.