layernorm-applied-per-vector-not-batch-dimension

IN premise — summaries/2026/08/24/wiki-Transformer_deep_learning_architecture-chunk-3.md

Created 2026-08-24T17:11:25+00:00

Layer normalization (LayerNorm) is applied element-wise across the feature dimension of a single token vector, not across the batch dimension, and is essential for numerical stability and convergence despite being conceptually unnecessary

Summary

LayerNorm works by standardizing each individual token's values against itself, not by comparing tokens to one another across the batch. This per-token design is what keeps the training process from blowing up or stalling, making it a practical necessity for the network to converge at all, even though the underlying equations don't technically require it.