  A principled understanding of generalization in deep learning may require
unifying disparate observations under a single conceptual framework. Previous
work has studied \emph{grokking}, a training dynamic in which a sustained
period of near-perfect training performance and near-chance test performance is
eventually followed by generalization, as well as the superficially similar
\emph{double descent}. These topics have so far been studied in isolation. We
hypothesize that grokking and double descent can be understood as instances of
the same learning dynamics within a framework of pattern learning speeds. We
propose that this framework also applies when varying model capacity instead of
optimization steps, and provide the first demonstration of model-wise grokking.
