When2Think: Learning Difficulty-Aware Length Control for Efficient Hybrid Reasoning Models
When2Think post-training framework allocates reasoning depth by problem difficulty, raising AIME24 Pass@3 10.0% while cutting token usage 27.9%.
When2Think is a post-training framework for hybrid reasoning that allocates computation by problem difficulty via Instance-level Difficulty-Aware Control (IDAC), a reward-shaping mechanism using pre-computed accuracy and token-usage reference statistics. It enables stable critic-free optimization without learned reward models or online reference-model queries, learning when to answer directly versus reason at length. On AIME24 it raises Pass@3 by 10.0% while reducing token usage 27.9% versus the base model, and reaches 40.0% Pass@3 on AIME25, beating compression and routing-only baselines.
- Formulates efficient reasoning as instance-adaptive computation allocation rather than uniform length penalties.
- IDAC reward shaping uses pre-computed accuracy and token statistics for critic-free optimization.
- AIME24 Pass@3 up 10.0% with 27.9% fewer tokens versus the base model.
- Reaches 40.0% Pass@3 on AIME25, beating compression and routing-only baselines.
From the original article
1 min read at huggingface.co
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.
By Jaejun Shim, HyunJin Kim, Young Jin Kim · huggingface.co
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