A new study shows that downstream task accuracy can be predicted directly from training budgets using a simple power law. The research, conducted by Apple Machine Learning Research, demonstrates that for a fixed token‑to‑parameter ratio, log accuracy scales reliably across multiple benchmarks.
The Signal
This finding could streamline model development worldwide, reducing costly trial‑and‑error cycles.