The direction of your training objective (forward-KL vs reverse-KL) fundamentally determines whether a model forgets old tasks—reverse-KL naturally avoids catastrophic forgetting while forward-KL requires replay to prevent it.
This paper explains why AI models forget old knowledge when trained on new tasks. Using mathematical analysis, the authors show that different training objectives (forward-KL vs reverse-KL) cause different types of forgetting, and that replaying old data helps prevent it. They also analyze three recent training methods to predict when they'll preserve old knowledge.