Why Your Norms Are Screaming at You
Activation norms, gradient norms, weight norms — what they tell you, why they explode or vanish, and every practical cause from hyperparameters to data bugs.
Activation norms, gradient norms, weight norms — what they tell you, why they explode or vanish, and every practical cause from hyperparameters to data bugs.
Activation norms, gradient norms, weight norms — what they tell you, why they explode or vanish, and every practical cause from hyperparameters to data bugs.
BERT used dropout everywhere. LLaMA uses none. The reason isn't that regularization stopped mattering — it's that at trillion-token scale, data diversity IS the regularizer.
Padding wastes GPU compute. Sequence packing eliminates it — but introduces cross-document attention contamination unless you explicitly mask it. Here's what the attention mask actually looks like.
BERT used dropout everywhere. LLaMA uses none. The reason isn't that regularization stopped mattering — it's that at trillion-token scale, data diversity IS the regularizer.
Padding wastes GPU compute. Sequence packing eliminates it — but introduces cross-document attention contamination unless you explicitly mask it. Here's what the attention mask actually looks like.