PAEC: Position-Aware Entropy Calibration for LLM Reasoning in RLVR
Reinforcement learning with verifiable rewards (RLVR) improves large language model reasoning but often suffers from rapid policy-entropy collapse, where the policy prematurely concentrates on narrow high-probability reasoning paths. While global entropy regularization can encourage exploration, uniformly increasing entropy across all token positions is inefficient for long reasoning trajectories, where many tokens are not decision-relevant. We propose Position-Aware Entropy Calibration (PAEC), a token-level entropy-management framework that constructs a soft mask from local top-p entropy and top-two candidate competition, and applies an anchor-based lower-bound penalty to prevent selecte...
| Authors | Shumeng Yang, Yisu Liu, Jiayi Zheng et al. |
|---|---|
| Primary category | cs.AI |
| Published | 7 June 2026 |
| Last updated at source | 7 June 2026 |
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