When Continual Learning Moves to Memory: A Study of Experience Reuse in LLM Agents
arXiv:2604.27003v1 Announce Type: new
Abstract: Memory-augmented LLM agents offer an appealing shortcut to continual learning: rather than updating model parameters, they accumulate experience in external memory, seemingly sidestepping the stability-p…