models even with the same sampling compute as the better quality of samples from the larger models
become the dominant factor for performance. These results present an interesting trade-oﬀ between
how much of the available compute should be used to train a model compared to sampling from it.
Both ways of leveraging more compute demonstrate log-linear scaling.
alphacode