[scarmig]

What they share is a subversion of the naive framework that ML works simply by performing gradient descent over a loss landscape. Double descent subverts it by showing that learning isn't monotonic in parameter count; grokking subverts it by learning after training convergence.

I'd put the lottery ticket hypothesis in the same bucket of "things that may happen that don't make sense at all for a simple optimization procedure."

[baq]

My takeaway from the paper is that you can guide training by adding/switching to a more difficult loss function after you got the basics right. Looks like they never got to overfitting grokking, so maybe there’s more to discover further down the training alley.