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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 β1,β2[0,1)\beta_1,\beta_2\in[0,1) can have average regret bounded away from zero. A similar result of Reddi-Kale-Kumar from 2018 required β1<β2\beta_1<\sqrt{\beta_2}. Similar to their result, we use a three-periodic sequence of linear functions on [1,1][-1,1] with slopes c,1,1c,-1,-1, though we use cc slightly larger than 22. 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.

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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}
}

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26 pages