This idea of machine-curated or machine-generated training data goes back thirty years or so under different names, at least in analogy. A classic thing to do

This idea of machine-curated or machine-generated training data goes back thirty years or so under different names, at least in analogy. A classic thing to do with ancient fraud detection models was a) train a large/complex model on a reasonably accurate dataset b) select training examples from the original training data and other examples that the original model gets right. c) train a simpler model on the filtered dataset This worked very well in the simpler world of simple classifiers particularly when the original training data had errors in it. Trying to fit these errors made the first model more complex (and still it often failed). The subset of training examples excluded many or even most of the erroneous examples and also excluded subtle cases that were impossible to learn within the complexity bounds of the time.