Author

Young Jin Kim

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Preprint Sep 2026

When2Think: Learning When and How Much to Reason

This work proposes When2Think, an RLVR-based post-training framework for instance-adaptive computation allocation that requires neither a learned reward model nor a learned critic, and offline reference caching avoids online reference-model queries during policy updates.

Jaejun Shim, Hyunjin Kim, Young Jin Kim et al. · 0 citations

#artificial intelligence Preprint Sep 2026

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 ac...

Jaejun Shim, Hyunjin Kim, Young Jin Kim et al. · 0 citations