Comment by epolanski
4 hours ago
> You use the previous gen model to prepare datasets for the next model iteration
I've read multiple times that this approach is harmful in training.
You're essentially describing what many call distillation, but it's only useful in post training to guide behavior, it teaches how to behave, not how to think.
I might be wrong though and would be glad if someone more knowledgeable provided more insights.
There have been papers about model collapse, but the underlying assumption is that you constantly train on only the outputs of the previous model. Later research has shown that as long as you retain some "real" data, training on largely synthetic data is ok.
And in the case the previous poster describes, the other model doesn't generate datasets, it generates environments which the next generation interact with to learn from.