What Makes Learned Knowledge Last in Continual Learning?

Catastrophic forgetting limits the ability of neural networks to learn multiple tasks sequentially. Continual learning methods address this problem by reducing the interference introduced by later tasks. However, once a sample is classified correctly, existing methods treat it as securely learned, without considering how easily that prediction may later be reversed. We show that predictions that are equally correct at task completion can differ substantially in their future retention. To capture this difference, we introduce acquisition stability (AS), which measures how often a correct prediction remains correct under mild, label-preserving input changes. In our experiments, low-AS predictions are forgotten up to 7.1× more than high-AS ones. Since AS can be measured while the current-task data are still available, fragile predictions can be identified and strengthened before future interference arrives. We therefore propose AS-CL, which (i) strengthens low-AS predictions before leaving a task, and (ii) gives greater replay weight to predictions that remain fragile during later tasks. Across three benchmarks, AS-CL consistently reduces forgetting, with the largest gains on low-AS predictions.