Training a neural network is an optimization problem: minimize a loss function over billions of parameters. The journey from vanilla gradient descent to Adam reveals why each step was necessary.
Adam is the default optimizer for language model training, but using it correctly — the right β values, weight decay, learning rate schedule — makes a larger difference than most people expect.
Training a neural network is an optimization problem: minimize a loss function over billions of parameters. The journey from vanilla gradient descent to Adam reveals why each step was necessary.
Adam is the default optimizer for language model training, but using it correctly — the right β values, weight decay, learning rate schedule — makes a larger difference than most people expect.