| title | Skaling: Chinchilla's Exponents Meet Kaplan's Coupling |
|---|---|
| authors | Mathurin Videau, Badr Youbi-Idrissi, David Lopez-Paz, Kartik Ahuja |
| date | 2026-08-07 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2608.07222 |
| affiliation | AI at Meta |
| upvotes | 15 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | Neural scaling laws are foundational for language model development, yet standard formulations systematically under- and overestimate loss at data-scarce and overtraining extremes. This failure originates in the underlying assumption that model size and training data impact the loss independently. To address this, we introduce the Skaling law, a generalized functional form that couples model capacity and data through a single interaction exponent. This simple extension reduces the Mean Absolute Percentage Error (MAPE) by 1.5-3x across both interpolation and extrapolation regimes. When paired with a sparse grid strategy restricted to low-compute regimes, the Skaling law achieves accurate full-grid extrapolation using approximately 10x less compute than uniform sweeps. By enabling reliable performance prediction from small-scale experiments, the Skaling law provides a more robust and resource-efficient framework for allocating compute budgets in next-generation model training. |
cite
@misc{videau2026skaling,
title={Skaling: Chinchilla's Exponents Meet Kaplan's Coupling},
author={Mathurin Videau and Badr Youbi-Idrissi and David Lopez-Paz and Kartik Ahuja},
year={2026},
eprint={2608.07222},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2608.07222}
}