Skaling: Chinchilla's Exponents Meet Kaplan's Coupling

TL;DR - An arXiv preprint proposing the "Skaling law," a generalized neural scaling law that couples model size and training data via a single interaction exponent, fixing systematic loss-prediction errors at data-scarce and overtrained extremes. It matters because better extrapolation lets teams allocate pretraining compute budgets from cheap small-scale runs.

• Diagnoses the core flaw in standard scaling laws as the assumption that model size and data affect loss independently; adds one interaction exponent to couple them.
• Reports 1.5–3x lower Mean Absolute Percentage Error (MAPE) than standard forms in both interpolation and extrapolation regimes.
• Combined with a sparse grid strategy limited to low-compute runs, it extrapolates to full-grid results using roughly 10x less compute than uniform sweeps.
• Framing bridges Chinchilla-style exponents and Kaplan-style coupling, targeting reliable performance prediction for next-generation training budget allocation.