  Work on continual learning (CL) has largely focused on the problems arising
from the dynamically-changing data distribution. However, CL can be decomposed
into two sub-problems: (a) shifts in the data distribution, and (b) dealing
with the fact that the data is split into chunks and so only a part of the data
is available to be trained on at any point in time. In this work, we look at
the latter sub-problem -- the chunking of data -- and note that previous
analysis of chunking in the CL literature is sparse. We show that chunking is
an important part of CL, accounting for around half of the performance drop
from offline learning in our experiments. Furthermore, our results reveal that
current CL algorithms do not address the chunking sub-problem, only performing
as well as plain SGD training when there is no shift in the data distribution.
We analyse why performance drops when learning occurs on chunks of data, and
find that forgetting, which is often seen to be a problem due to distribution
shift, still arises and is a significant problem. Motivated by an analysis of
the linear case, we show that per-chunk weight averaging improves performance
in the chunking setting and that this performance transfers to the full CL
setting. Hence, we argue that work on chunking can help advance CL in general.

Machine Learning;Machine Learning