Normalization refers to techniques (LayerNorm, RMSNorm, BatchNorm) that rescale activations during forward passes to stabilize training and improve convergence. Where and how you normalize significantly affects training dynamics — pre-norm (before attention/FFN) is now standard in large transformers, and RMSNorm has largely replaced LayerNorm for its simplicity and speed.