Scaling laws are empirical observations that loss decreases predictably as you scale parameters, data, and compute. Kaplan et al.'s 2020 OpenAI paper and DeepMind's follow-up Chinchilla Optimal result are the field's landmark works. Their practical importance is that frontier labs can plan Compute and data investments years ahead with reasonable confidence. The pure scale-is-all-you-need debate has matured into a more refined conversation about Test-time Compute and data quality.
MEVZU N°124ISTANBULYEAR I — VOL. III
Glossary · Advanced · 2020
Scaling Laws
Empirical relationships describing how model performance changes with parameters, data, and compute.
- EN — English term
- Scaling Laws
- TR — Turkish term
- Ölçeklendirme Yasaları