ModelRefs / Neural Scaling — AI Glossary
Neural Scaling — AI Glossary
The study of how model performance improves predictably as compute, parameters, and data increase according to power laws. Also called scaling.
Overview
Neural scaling refers to the empirical finding that loss decreases as a smooth power law of training compute, model size, and dataset tokens. It underpins the race to train ever-larger models and guides investment decisions. Key papers: Kaplan et al. 2020, Hoffmann et al. 2022 (Chinchilla), Muennighoff et al. 2023.
Reference details
| Topic | architecture |
|---|---|
| Also known as | scaling |
| Last reviewed | 2026-06-24 |
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Frequently asked questions
What is Neural Scaling?
The study of how model performance improves predictably as compute, parameters, and data increase according to power laws.
Is Neural Scaling the same as scaling?
Yes — scaling are common aliases for Neural Scaling.
What concepts are related to Neural Scaling?
Closely related concepts include scaling law, chinchilla, parameter count.