This is correct. To give more synthetic data examples:

1. Generate inverted problems which are easier to produce than solve. For instance, create an integration math exercise by differentiating a hairy function and reversing the steps.

2. Create (simulated) environmental data.

3. Use adversarial model competition, e.g. self-playing chess or training an artificial image generator/detector model pair.

There's evidently a commonplace myth that information quality starts pristine and exclusively gets degraded by systems thereafter. That's just easily, demonstrably false in myriad ways. That's why it's absurd to conclude that LLM output being in internet training data will cause model collapse.

Information-rich synthetic data can be created without humans, and it works. (Check out Phi, for instance.)