Why We Should Be Cautious About Scaling Laws in Deep Learning

The article reviews the history, theory, and empirical findings of scaling laws for neural language models, compares the Kaplan and Chinchilla formulations, discusses data‑limited regimes and fitting subtleties, and highlights why careful interpretation and resource allocation are essential for reliable predictions.

Data EfficiencyDeep LearningKaplan

0 likes · 26 min read

Why We Should Be Cautious About Scaling Laws in Deep Learning

PaperAgent

Jun 26, 2026 · Artificial Intelligence

Lilian Weng’s Deep Dive into Scaling Laws for Large‑Model Training

The article explains how scaling laws serve as a budget guide for training large language models, comparing Kaplan’s and Chinchilla’s findings, illustrating optimal parameter‑token trade‑offs, and highlighting the impact of data quality and duplication on model performance.

Compute BudgetData QualityKaplan

0 likes · 9 min read

Lilian Weng’s Deep Dive into Scaling Laws for Large‑Model Training