scaling-laws-power-law-relationships
IN premise — entries/2026/06/21/wiki-Generative_pre-trained_transformer-chunk-1.md
Created 2026-06-21T09:50:09+00:00
Neural scaling laws describe empirical power-law relationships between model performance and model size, dataset size, and compute (Kaplan et al., 2020).
Summary
There is a predictable mathematical pattern linking how large a model is, how much data it trains on, how much compute it uses, and how well it ultimately performs. This matters because it lets the system estimate expected performance from a resource budget — or figure out how much compute or data is needed to hit a target capability — without having to build and test every configuration from scratch.
Dependents
These beliefs depend on this one:
- IN scaling-evidence-is-itself-empirical-validating-craft-methodology — Key scaling relationships in LLM research — such as power-law relationships between performance and resources (Kaplan et al., 2020) and Chinchilla's information-theoretic grounding of compute-optimal scaling — were discovered through empirical observation rather than first-principles derivation. That these foundational quantitative regularities emerged from empirical methods is consistent with the field's broader character as a craft discipline where core knowledge is discovered and transmitted experientially.