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Complex learning rate schedules have become an integral part of deeplearning. We find empirically that common fine-tuned schedules decay thelearning rate after the weight norm bounces. This leads to the proposal ofABEL: an automatic scheduler which decays the learning rate by keeping track ofthe weight norm. ABEL's performance matches that of tuned schedules and is morerobust 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 simplifyschedules even further with no loss in performance. In such cases, a complexschedule has similar performance to a constant learning rate with a decay atthe end of training.

How to decay your learning rate