Rethinking Token-Level Credit Assignment in RLVR: A Polarity-Entropy Analysis
For researchers improving LLM reasoning via RLVR, this work provides a theoretical and practical method to address credit assignment, but the gains are incremental over existing methods.
The paper identifies a credit assignment problem in RLVR due to sparse outcome-based rewards, and proposes Entropy-Aware Policy Optimization (EAPO) which modulates token-level learning signals based on entropy. EAPO outperforms strong baselines across two model families.
Reinforcement Learning with Verifiable Rewards (RLVR) has substantially improved the reasoning ability of Large Language Models (LLMs). However, its sparse outcome-based rewards pose a fundamental credit assignment problem. We analyze this problem through the joint lens of reward polarity and token entropy. Our diagnostic tool, the Four Quadrant Decomposition, isolates token updates by polarity and entropy, and controlled ablations show that reasoning improvements concentrate in the high-entropy quadrants. To justify this observation theoretically, we adapt Conditional Mutual Information to the autoregressive RLVR setting and prove that the credit a token can carry is upper-bounded by its entropy. This view yields testable predictions that reasoning gains arise primarily from high-entropy tokens, with unique roles for positive and negative updates. A gradient analysis of GRPO further reveals how uniform reward broadcast dilutes signal at high-entropy positions while over-crediting deterministic tokens. Grounded in these insights, we propose Entropy-Aware Policy Optimization (EAPO) that modulates token-level learning signals accordingly. Extensive experiments demonstrate that EAPO outperforms strong baselines across two model families.