  Although gradient descent with momentum is widely used in modern deep
learning, a concrete understanding of its effects on the training trajectory
still remains elusive. In this work, we empirically show that momentum gradient
descent with a large learning rate and learning rate warmup displays large
catapults, driving the iterates towards flatter minima than those found by
gradient descent. We then provide empirical evidence and theoretical intuition
that the large catapult is caused by momentum "amplifying" the
self-stabilization effect (Damian et al., 2023).B.1

Machine Learning;Optimization and Control;Machine Learning