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Heterogeneous Adaptive Policy Optimization: Tailoring Optimization to Every Token’s Nature
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 22:04
摘要
Using entropy as a measure of heterogeneity to guide optimization has emerged as a crucial research direction in Reinforcement Learning for LLMs. However, existing methods typically treat it as a discrete filter or post-hoc regulator rather than a core optimization driver. To fully leverage the potential of entropy and achieve fine-grained regulation, we introduce Heterogeneous Adaptive Policy Optimization (HAPO), a token-aware algorithm that continuously adapts optimization dynamics based on token-level entropy throughout the entire training process. Our algorithm includes four key components: (1) Adaptive Temperature Sampling that adjusts sampling temperature in real time, promoting exploration at high-entropy tokens. (2) Token-Level Group Average Advantage Estimation that estimates advantages at token level, accounting for sequence-length effects while preserving non-biased treatment.(3) Differential Advantage Redistribution that leverages entropy and importance ratios to adjust advantages for tokens with clear signals. (4) Asymmetric Adaptive Clipping that dynamically adjusts clipping boundaries based on token-level entropy. Through systematic investigation of entropy, we embed token-level treatment into every stage. Extensive experiments on mathematical reasoning, code, and logic tasks across multiple models demonstrate HAPO’s consistent superiority over DAPO.
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记录 #1 · 2026-07 · 最新观测版本
Heterogeneous Adaptive Policy Optimization: Tailoring Optimization to Every Token’s Nature
Using entropy as a measure of heterogeneity to guide optimization has emerged as a crucial research direction in Reinforcement Learning for LLMs. However, existing methods typically treat it as a discrete filter or post-hoc regulator rather than a core optimization driver. To fully leverage the potential of entropy and achieve fine-grained regulation, we introduce Heterogeneous Adaptive Policy Optimization (HAPO), a token-aware algorithm that continuously adapts optimization dynamics based on token-level entropy throughout the entire training process. Our algorithm includes four key components: (1) Adaptive Temperature Sampling that adjusts sampling temperature in real time, promoting exploration at high-entropy tokens. (2) Token-Level Group Average Advantage Estimation that estimates advantages at token level, accounting for sequence-length effects while preserving non-biased treatment.(3) Differential Advantage Redistribution that leverages entropy and importance ratios to adjust advantages for tokens with clear signals. (4) Asymmetric Adaptive Clipping that dynamically adjusts clipping boundaries based on token-level entropy. Through systematic investigation of entropy, we embed token-level treatment into every stage. Extensive experiments on mathematical reasoning, code, and logic tasks across multiple models demonstrate HAPO’s consistent superiority over DAPO.
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