  Learning from non-stationary data streams and overcoming catastrophic
forgetting still poses a serious challenge for machine learning research.
Rather than aiming to improve state-of-the-art, in this work we provide insight
into the limits and merits of rehearsal, one of continual learning's most
established methods. We hypothesize that models trained sequentially with
rehearsal tend to stay in the same low-loss region after a task has finished,
but are at risk of overfitting on its sample memory, hence harming
generalization. We provide both conceptual and strong empirical evidence on
three benchmarks for both behaviors, bringing novel insights into the dynamics
of rehearsal and continual learning in general. Finally, we interpret important
continual learning works in the light of our findings, allowing for a deeper
understanding of their successes.
