Two seemingly separate challenges in artificial intelligence research turn out to be the same problem. On one side, continual learning researchers study catastrophic forgetting — how models lose knowledge of earlier tasks when trained on new ones. On the other side, AI safety researchers observe that fine-tuning erodes safety behaviors, even with benign data. According to a new analysis published on LessWrong, both failures stem from the same mechanism: gradient interference during training. When a model learns a new objective, the gradients can overwrite what it learned before. The practical implication is significant: techniques that protect old knowledge in continual learning — like elastic weight consolidation — could safeguard safety behaviors. Both communities could also share monitoring tools, watching the loss landscape itself during training rather than assessing damage afterward.

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