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Rehearsal, a cornerstone of continual learning, can paradoxically *hurt* a model's ability to adapt to new tasks, challenging the conventional wisdom that more rehearsal is always better.
Forget about complex continual learning strategies: this work shows that simply tuning the *magnitude* of parameter updates can significantly reduce forgetting and improve generalization.
