Beyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learning

arXiv:2604.15414v3 Announce Type: replace-cross Abstract: Continual reinforcement learning must balance retention with adaptation, yet many methods still rely on \emph{single-model preservation}, committing to one evolving policy as the main reusable solution

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