Synthetic data loops: are we heading for model collapse or self-improvement?

The theory papers warn that training on model outputs degrades the tails of the distribution, but frontier labs are clearly doing it at massive scale and capabilities keep rising. Either the collapse concern is overblown, or the labs have filtering tricks they aren't publishing. I've seen both claims from credible people. What's the actual state of evidence on recursive self-training?