What are Scaling Laws?

Scaling Laws are predictable mathematical relationships that describe how AI model performance improves as factors like model size, training data, and computing power increase. These empirical patterns, particularly prominent in large language models, show that bigger models trained on more data with more computation tend to perform better in consistent, measurable ways. Scaling Laws help researchers forecast AI capabilities and determine optimal resource allocation for training increasingly powerful models.