PAEC: Position-Aware Entropy Calibration for LLM Reasoning in RLVR

Researchers propose Position-Aware Entropy Calibration (PAEC), a novel technique that selectively manages entropy in reinforcement learning systems used to improve large language model reasoning. The method addresses policy-entropy collapse by applying targeted entropy penalties only at decision-critical token positions rather than uniformly across all tokens, demonstrating improved performance on mathematical reasoning benchmarks.

PAEC addresses a fundamental challenge in reinforcement learning applied to language models: the tendency of policies to prematurely converge to narrow solution paths, limiting exploration and reducing reasoning quality. Traditional global entropy regularization indiscriminately increases randomness across all tokens, which is computationally wasteful in long reasoning sequences where most tokens follow deterministic patterns. By implementing position-aware entropy management, researchers enable more efficient exploration allocation where it matters most—at decision points that meaningfully affect reasoning outcomes.

This work builds on the broader RLVR framework, which has shown promise in enhancing LLM reasoning by leveraging verifiable rewards. However, prior approaches struggled with exploration-exploitation tradeoffs. PAEC's innovation lies in its token-level granularity, using local entropy statistics and candidate competition metrics to identify which positions warrant exploration encouragement. The anchor-based lower-bound penalty mechanism prevents selective collapse while avoiding uniform inefficiency.

The experimental validation across five mathematical reasoning benchmarks—with notable gains on RIME-style problems—demonstrates practical utility. For AI researchers and practitioners building reasoning systems, PAEC offers a concrete methodology for improving model performance without architectural changes. The findings suggest entropy management in reasoning RL requires task-aware, position-specific calibration rather than blanket exploration strategies.

Future development likely involves extending this approach to other domains requiring complex reasoning chains and exploring whether position-aware entropy management transfers across different model architectures and problem types.

• →PAEC uses token-level entropy management rather than global regularization, improving efficiency in long reasoning chains.
• →The method constructs soft masks from local top-p entropy and candidate competition to identify decision-critical positions.
• →Experimental results show macro-average majority-vote improvements over RLVR baselines, especially on AIME-style mathematical tasks.
• →Position-aware entropy calibration represents a shift from uniform exploration to selective, task-aware entropy allocation strategies.
• →The approach maintains policy exploration without architectural modifications, suggesting broader applicability to reasoning RL systems.