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Forget catastrophic forgetting: modular memory, blending in-context and in-weight learning, offers a practical path to truly continual learning agents.
Weight regularization, often overlooked in parameter-efficient continual learning, can still significantly improve the stability-plasticity trade-off, even when using low-rank adapters.