  Learned optimizers are algorithms that can themselves be trained to solve
optimization problems. In contrast to baseline optimizers (such as momentum or
Adam) that use simple update rules derived from theoretical principles, learned
optimizers use flexible, high-dimensional, nonlinear parameterizations.
Although this can lead to better performance in certain settings, their inner
workings remain a mystery. How is a learned optimizer able to outperform a well
tuned baseline? Has it learned a sophisticated combination of existing
optimization techniques, or is it implementing completely new behavior? In this
work, we address these questions by careful analysis and visualization of
learned optimizers. We study learned optimizers trained from scratch on three
disparate tasks, and discover that they have learned interpretable mechanisms,
including: momentum, gradient clipping, learning rate schedules, and a new form
of learning rate adaptation. Moreover, we show how the dynamics of learned
optimizers enables these behaviors. Our results help elucidate the previously
murky understanding of how learned optimizers work, and establish tools for
interpreting future learned optimizers.
