Researchers fit trends across controlled training runs and use them to reason about resource allocation or expected loss. The relationships apply within observed regimes and can change with architecture, data quality, or evaluation.
Scaling laws are empirical relationships between model performance and factors such as compute, data, and parameter count.
Researchers fit trends across controlled training runs and use them to reason about resource allocation or expected loss. The relationships apply within observed regimes and can change with architecture, data quality, or evaluation.