essay · 2026-09-23

Chinchilla scaling law parameters dataset size ratio

Research into language model efficiency reveals that earlier studies produced conflicting predictions for optimal model sizes because they used different definitions of what constitutes a "parameter," specifically excluding embedding layers in one major framework while including them in another. This distinction creates two distinct power laws where the number of parameters scales with compute budget $C$ as 0.73 versus 0.50, leading to significant uncertainty for practitioners trying to determine the right model size and training data volume. By acknowledging that these discrepancies stem from counting non-embedding rather than total parameters, researchers can reconcile these findings and establish a more reliable guide for scaling models efficiently without overfitting or underutilizing resources.

Sources

• Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws — 2023-12-31T10:53:58Z
• Evaluating the Robustness of Chinchilla Compute-Optimal Scaling — 2025-09-28T16:41:01Z
• Reconciling Kaplan and Chinchilla Scaling Laws — 2024-06-12T13:30:48Z
• Chinchilla Scaling: A replication attempt - arXiv.org
• Evaluating the Robustness ofChinchilla Compute-Optimal Scaling
• [2404.10102] Chinchilla Scaling: A replication attempt - arXiv.org
• Skaling: Chinchilla's Exponents Meet Kaplan's Coupling
• Tamay Besiroglu Ege Erdil Matthew Barnett Josh You - arXiv.org