Synthetic data and the self-improvement loop: a model trains on its own output to improve — or collapses without a filter

You've run out of human data. So the model makes its own, grades it, and retrains on it. Does that actually work? I built two browser demos that answer both sides of the question, and the whole thing hinges on one word: filter . With a verifier in the loop, quality climbs each round. Without it, the model slowly eats its own tail until it collapses to a single note. Same engine, opposite outcomes. The asymmetry that makes it possible For many tasks, verifying an answer is far cheaper than…