Why did Kaplan’s scaling law predict a different optima...

The discrepancy arises because Kaplan’s experimental design did not train models to their natural termination points. Furthermore, the use of truncated cosine learning rate schedules meant the data was biased toward shorter, data-light runs. Chinchilla corrected these methodological issues, resulting in a more accurate assessment of the optimal parameter-to-token ratio.