  We give a new separation result between the generalization performance of
stochastic gradient descent (SGD) and of full-batch gradient descent (GD) in
the fundamental stochastic convex optimization model. While for SGD it is
well-known that $O(1/\epsilon^2)$ iterations suffice for obtaining a solution
with $\epsilon$ excess expected risk, we show that with the same number of
steps GD may overfit and emit a solution with $\Omega(1)$ generalization error.
Moreover, we show that in fact $\Omega(1/\epsilon^4)$ iterations are necessary
for GD to match the generalization performance of SGD, which is also tight due
to recent work by Bassily et al. (2020). We further discuss how regularizing
the empirical risk minimized by GD essentially does not change the above
result, and revisit the concepts of stability, implicit bias and the role of
the learning algorithm in generalization.
