Meta FAIR paper reveals limitations in Chinchilla scaling law, proposes fix that cuts compute costs by 10x

For the past few years, DeepMind’s Chinchilla scaling law has been the unofficial gospel of large language model training. It told researchers how to balance model size against training data to get the best performance per unit of compute. Meta’s FAIR lab just published a paper arguing that Chinchilla gets the relationship fundamentally wrong, and that fixing it could slash the compute needed to plan model training by roughly 10x. The paper, titled “Skaling: Chinchilla’s Exponents Meet Kaplan’s Coupling,” was published on August 7, 2026 by Mathurin Videau, Badr Youbi-Idrissi, David Lopez-Paz, and Kartik Ahuja. It introduces a modified scaling law that accounts for something Chinchilla quietly ignores: the interaction between model size and training data volume. What Chinchilla gets wrong DeepMind’s Chinchilla scaling law, established in March 2022, treats model parameters (N) and training tokens (D) as independent variables that each contribute separately to a model’s final loss. The Skaling paper demonstrates that this independence assumption breaks down in practice. By computing the mixed partial derivative of the loss surface with respect to N and D, the researchers showed it’s ...