optimizers
Everything on Ground Truth tagged “optimizers” — 2 items.
Optimizers: how Adam and AdamW turn gradients into learning Lesson
An optimizer is the rule that decides how a neural network changes its weights after each mistake; Adam and its refinement AdamW became the default because they adapt the step size for every weight, making training faster and far less finicky than plain gradient descent.
A giant benchmark tested 24 optimizers - and AdamW's edge held up News
OmniOpt ran a controlled bake-off of more than two dozen modern training optimizers across model sizes from 60M to 1B parameters, and its main lesson is deflating: no challenger cleanly dethrones AdamW, because an optimizer's advantage depends heavily on scale, task, and tuning budget.