[D] Has double descent/grokking changed how people train models?

These papers indicate that when a large model is trained on a small dataset for a very long time, the test loss first goes down, then up when it overfits, but eventually back down even lower, and the model generalizes correctly.

Do people take advantage of this in practice to get good, generalized models on small datasets? Do people often train longer now in order to get better models? Or has this not caught on in practice for some reason? …

machinelearning people