SHAPE：基于潜在估计的阶段感知分层优势用于大语言模型推理
SHAPE: Stage-aware Hierarchical Advantage via Potential Estimation for LLM Reasoning
AI总结：
SHAPE通过潜在估计引入分层信用分配机制，提升大语言模型推理的效率与准确性，实验显示在数学推理任务中平均准确率提升3%，token消耗减少30%。
摘要：
过程监督已成为增强大语言模型推理能力的有前景方法，但现有方法未能区分有意义的进步与单纯冗长，导致推理能力有限和未解决的token效率问题。为此，我们提出了基于潜在估计的阶段感知分层优势（SHAPE）框架，将推理形式化为通过经验可解状态空间的轨迹。SHAPE引入了分层信用分配机制：在段级层面，采用阶段感知优势函数优先处理低潜力状态中的高效突破；在token层面，利用熵驱动的再分配来增强执行信号。在三种基础模型和五个基准测试中的数学推理任务中，SHAPE实现了平均准确率提升3%且token消耗减少30%。
英文摘要：
Process supervision has emerged as a promising approach for enhancing LLM reasoning, yet existing methods fail to distinguish meaningful progress from mere verbosity, leading to limited reasoning capabilities and unresolved token inefficiency. To address this, we propose Stage-aware Hierarchical Advantage via Potential Estimation (SHAPE), a framework that formalizes reasoning as a trajectory through a state space of empirical solvability. SHAPE introduces a hierarchical credit assignment mechanism: at the segment level, it employs a stage-aware advantage function to prioritize efficient breakthroughs in low-potential states; at the token level, it utilizes entropy-driven redistribution to sharpen execution signals. Extensive experiments in math reasoning across three base models and five benchmarks demonstrate that SHAPE achieves an average accuracy gain of 3% with 30% reduced token consumption.
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