  Complex learning rate schedules have become an integral part of deep
learning. We find empirically that common fine-tuned schedules decay the
learning rate after the weight norm bounces. This leads to the proposal of
ABEL: an automatic scheduler which decays the learning rate by keeping track of
the weight norm. ABEL's performance matches that of tuned schedules and is more
robust with respect to its parameters. Through extensive experiments in vision,
NLP, and RL, we show that if the weight norm does not bounce, we can simplify
schedules even further with no loss in performance. In such cases, a complex
schedule has similar performance to a constant learning rate with a decay at
the end of training.
