On the Convergence of Adam, Revisited
Machine Learning
2026-07-03 v1 Optimization and Control
Machine Learning
Abstract
We show that projected Adam for online optimization with arbitrary moment decay parameters can have average regret bounded away from zero. A similar result of Reddi-Kale-Kumar from 2018 required . Similar to their result, we use a three-periodic sequence of linear functions on with slopes , though we use slightly larger than . This nonzero average regret result extends to Adam variants such as AdamW, RMSProp, NAdam, Adan, AdaMax, Muon, and to an i.i.d. variant of the three-periodic sequence of slopes for Adam.
Keywords
Cite
@article{arxiv.2607.03519,
title = {On the Convergence of Adam, Revisited},
author = {Steven Heilman and Sampad Mohanty},
journal= {arXiv preprint arXiv:2607.03519},
year = {2026}
}
Comments
26 pages