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The Slingshot Mechanism: An Empirical Study of Adaptive Optimizers and the \emph{Grokking Phenomenon}
Vimal Thilak · Etai Littwin · Shuangfei Zhai · Omid Saremi · Roni Paiss · Joshua Susskind
Event URL: https://openreview.net/forum?id=lY1e0PNkSJ »

The \emph{grokking phenomenon} reported by Power et al.~\cite{power2021grokking} refers to a regime where a long period of overfitting is followed by a seemingly sudden transition to perfect generalization. In this paper, we attempt to reveal the underpinnings of Grokking via empirical studies. Specifically, we uncover an optimization anomaly plaguing adaptive optimizers at extremely late stages of training, referred to as the \emph{Slingshot Mechanism}. A prominent artifact of the Slingshot Mechanism can be measured by the cyclic phase transitions between stable and unstable training regimes, and can be easily monitored by the cyclic behavior of the norm of the last layers weights. We empirically observe that without explicit regularization, Grokking as reported in \cite{power2021grokking} almost exclusively happens at the onset of \emph{Slingshots}, and is absent without it. While common and easily reproduced in more general settings, the Slingshot Mechanism does not follow from any known optimization theories that we are aware of, and can be easily overlooked without an in depth examination. Our work points to a surprising and useful inductive bias of adaptive gradient optimizers at late stages of training, calling for a revised theoretical analysis of their origin.

Author Information

Vimal Thilak (Apple)
Etai Littwin (Apple)
Shuangfei Zhai (Apple)
Omid Saremi (Apple)
Roni Paiss
Joshua Susskind (Apple Inc.)

I was an undergraduate in Cognitive Science at UCSD from 1995-2003 (with some breaks). Then I earned a PhD from UofT in machine learning and cognitive neuroscience, with Dr. Geoff Hinton and Dr. Adam Anderson. Following grad school I moved to UCSD for a post-doctoral position. Before coming to Apple I co-founded Emotient in 2012 and led the deep learning effort for facial expression and demographics recognition. Since joining Apple, I led the Face ID neural network team responsible for face recognition, and then started a machine learning research group within the hardware organization focused on fundamental ML technology.

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