<ul><li>Understanding the efficacy of Adam in training transformer-based language models is a key focus for the optimization community.</li><li>Multiple simplifications of Adam have been proposed, including signed gradient and signed momentum methods, to gain deeper insights.</li><li>An empirical study involving training over 1,300 language models compared Adam to simplified variants, revealing that constraining the Adam momentum parameters to be equal holds promise for optimal performance.</li><li>This constrained Adam option not only delivers robust performance but also offers new theoretical insights by implementing a natural online algorithm for estimating gradients' mean and variance.</li></ul>

In Search of Adam's Secret Sauce

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