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Solving a problem with a deep learning model requires researchers to optimizethe loss function with a certain optimization method. The research communityhas developed more than a hundred different optimizers, yet there is scarcedata on optimizer performance in various tasks. In particular, none of thebenchmarks test the performance of optimizers on source code-related problems.However, existing benchmark data indicates that certain optimizers may be moreefficient for particular domains. In this work, we test the performance ofvarious optimizers on deep learning models for source code and find that thechoice of an optimizer can have a significant impact on the model quality, withup to two-fold score differences between some of the relatively well-performingoptimizers. We also find that RAdam optimizer (and its modification with theLookahead envelope) is the best optimizer that almost always performs well onthe tasks we consider. Our findings show a need for a more extensive study ofthe optimizers in code-related tasks, and indicate that the ML4SE communityshould consider using RAdam instead of Adam as the default optimizer forcode-related deep learning tasks.

Judging Adam: Studying the Performance of Optimization Methods on ML4SE Tasks