That paper has had a pretty turbulent reception and looks pretty conclusively wrong at this point.

It used an incorrect theoretical framing that assumed that data was being replaced rather than accumulated as a result of more training (see https://arxiv.org/abs/2404.01413 which explores this). This is incorrect because this simply isn't how real-world datasets are created via synthetic data generation (which generally accumulate more data over time rather than replace their data). As a result most of the theoretical results were invalid.

Empirical evidence has also cast a considerable amount of doubt on the paper. For example, Microsoft Phi-4 was an empirical test in specifically what happens if the majority of your training data is synthetic rather than human and it turns out that Phi-4 did significantly better than previous models which relied primarily on human data.

There's some nuance to all of this in how exactly you do this, but the original claims of the paper are looking really shaky at this point.