The Road Less Scheduled

November 06, 2024

Abstract

Existing learning rate schedules that do not require specification of the optimization stopping step T are greatly out-performed by learning rate schedules that depend on T. We propose an approach that avoids the need for this stopping time by eschewing the use of schedules entirely, while exhibiting state-of-the-art performance compared to schedules across a wide family of problems ranging from convex problems to large-scale deep learning problems. Our Schedule-Free approach introduces no additional hyper-parameters over standard optimizers with momentum. Our method is a direct consequence of a new theory we develop that unifies scheduling and iterate averaging. Schedule-Free AdamW is the core algorithm behind our winning entry to the MLCommons 2024 AlgoPerf Algorithmic Efficiency Challenge Self-Tuning track.

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AUTHORS

Written by

Aaron Defazio

Alice Yang

Harsh Mehta

Konstantin Mishchenko

Ahmed Khaled

Ashok Cutkosky

Publisher

NeurIPS

Research Topics

Theory

Core Machine Learning

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