Scaling laws

Scaling laws are empirical relationships showing that a model's performance improves predictably as model size, training data, and compute increase, used to plan how large models should be trained.

Scaling laws sits in the Foundations part of the Agentik {OS} glossary, which defines the words used to build and run AI agent systems.

Also called Neural scaling laws, Chinchilla scaling.