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.