Researchers introduced When2Think, a difficulty-aware length control method for hybrid reasoning models that aims to stop large reasoning models from overthinking easy problems and underthinking hard ones. The work targets the efficiency tax imposed by existing approaches that rely on uniform length penalties or rigid routing, which trade reduced computation on easy queries for degraded performance on hard ones. No specific benchmark figures, dates, or author names were provided in the available source text.
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 e
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