Emergent Hierarchical Reasoning in LLMs through Reinforcement Learning
Authors: name: Haozhe Wang, name: Qixin Xu, name: Che Liu, name: Junhong Wu, name: Fangzhen Lin, name: Wenhu Chen

Reinforcement Learning (RL) has proven highly effective at enhancing the
complex reasoning abilities of Large Language Models (LLMs), yet underlying
mechanisms driving this success remain largely opaque. Our analysis reveals
that puzzling phenomena like ``aha moments", ``length-scaling'' and entropy
dynamics are not disparate occurrences but hallmarks of an emergent reasoning
hierarchy, akin to the separation of high-level strategic planning from
low-level procedural execution in human cognition. We uncover a compelling
two-phase dynamic: initially, a model is constrained by procedural correctness
and must improve its low-level skills. The learning bottleneck then decisively
shifts, with performance gains being driven by the exploration and mastery of
high-level strategic planning. This insight exposes a core inefficiency in
prevailing RL algorithms like GRPO, which apply optimization pressure
agnostically and dilute the learning signal across all tokens. To address this,
we propose HIerarchy-Aware Credit Assignment (HICRA), an algorithm that
concentrates optimization efforts on high-impact planning tokens. HICRA
significantly outperforms strong baselines, demonstrating that focusing on this
strategic bottleneck is key to unlocking advanced reasoning. Furthermore, we
validate semantic entropy as a superior compass for measuring strategic
exploration over misleading metrics such as token-level entropy.