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).

翻译：尽管带动量的梯度下降被广泛应用于现代深度学习，但其对训练轨迹的具体影响仍然难以捉摸。在本工作中，我们通过实证表明，采用大学习率与学习率热启动的动量梯度下降会显示出大型弹射效应，驱动迭代点趋向比梯度下降所发现的更平坦的极小值。随后，我们提供实证证据与理论直觉，指出大型弹射效应是由动量"放大"自稳定效应（Damian 等人，2023）所引发的。