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Adaptive gradient strategies, notably Adam, have develop into indispensable for optimizing neural networks, notably along with Transformers. On this paper, we current a novel optimization anomaly referred to as the Slingshot Impact, which manifests throughout extraordinarily late phases of coaching. We determine a particular attribute of this phenomenon by way of cyclic part transitions between steady and unstable coaching regimes, as evidenced by the cyclic habits of the norm of the final layer’s weights. Though the Slingshot Impact might be simply reproduced in additional common settings, it doesn’t align with any identified optimization theories, emphasizing the necessity for in-depth examination.
Furthermore, we make a noteworthy statement that Grokking happens predominantly through the onset of the Slingshot Results and is absent with out it, even within the absence of express regularization. This discovering suggests a stunning inductive bias of adaptive gradient optimizers at late coaching phases, urging a revised theoretical evaluation of their origin.
Our examine sheds gentle on an intriguing optimization habits that has important implications for understanding the inside workings of adaptive gradient strategies.
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