AI RESEARCH
From Passive Reuse to Active Reasoning: Grounding Large Language Models for Neuro-Symbolic Experience Replay
arXiv CS.AI
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ArXi:2605.09419v1 Announce Type: new While experience replay is essential for data efficiency in reinforcement learning (RL), standard methods treat the replay buffer as a passive memory system, prioritizing samples based on numerical prediction errors rather than their semantic significance. This approach stands in contrast to human learning, which accelerates mastery by actively abstracting fragmented experiences into behavioral rules.