AIBullisharXiv – CS AI · 7h ago6/10
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Revisiting Entropy Regularization: Adaptive Coefficient Unlocks Its Potential for LLM Reinforcement Learning
Researchers propose Adaptive Entropy Regularization (AER), a dynamic framework that addresses policy entropy collapse in LLM reinforcement learning by adjusting exploration intensity based on task difficulty. The method improves upon fixed entropy regularization approaches, demonstrating consistent gains in mathematical reasoning benchmarks while maintaining balanced exploration-exploitation tradeoffs.