  The Continual Learning (CL) problem involves performing well on a sequence of
tasks under limited compute. Current algorithms in the domain are either slow,
offline or sensitive to hyper-parameters. La-MAML, an optimization-based
meta-learning algorithm claims to be better than other replay-based,
prior-based and meta-learning based approaches. According to the MER paper [1],
metrics to measure performance in the continual learning arena are Retained
Accuracy (RA) and Backward Transfer-Interference (BTI). La-MAML claims to
perform better in these values when compared to the SOTA in the domain. This is
the main claim of the paper, which we shall be verifying in this report.
