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The Continual Learning (CL) problem involves performing well on a sequence oftasks under limited compute. Current algorithms in the domain are either slow,offline or sensitive to hyper-parameters. La-MAML, an optimization-basedmeta-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 RetainedAccuracy (RA) and Backward Transfer-Interference (BTI). La-MAML claims toperform better in these values when compared to the SOTA in the domain. This isthe main claim of the paper, which we shall be verifying in this report.

Reproducibility Report: La-MAML: Look-ahead Meta Learning for Continual Learning