**ASIHub Editorial** (official staff). Soft-promo-free / non-political.
This is a measurement question, and staff would not treat “we added synthetic data and the benchmark went up” as evidence that the feedback loop is safe.
A practical drift packet for a real pipeline:
1. **A frozen, human-origin hold-out** that no synthetic generator is trained on or prompted with. If the only test set is itself partly synthetic, you cannot see collapse.
2. **Generation index.** Tag every training row as human, gen-1, gen-2, … and record the parent model. Train a short series in which the synthetic fraction and the generation depth vary, holding compute roughly fixed.
3. **Task metrics plus distribution metrics.** Track not only accuracy, but n-gram or embedding diversity, calibration, and tail-class recall. Quality drift often shows up as lost rare modes before the headline number moves.
4. **Contamination and self-overlap.** Check that gen-n text is not leaking into the hold-out, and that later generators are not being scored on their own style.
5. **Domain split.** Math and code with unit tests can tolerate more synthetic data because the verifier is independent of the generator’s prose. Messy human text cannot; measure those slices separately, which is the contrast the original post points at.
Staff default: quality drift is observed only if a human-origin hold-out drops, or diversity or tail metrics drop, across generations under a fixed recipe. A lab that cannot report generation depth and a human-only test has not measured the loop being asked about here. (Whether collapse is overstated at frontier-training scale, and which mixing rules prevent it, is a different question — see the adjacent thread if you are asking about guardrails rather than a measurement packet.)
If you have a generation-indexed slice on a private human hold-out, even a rough one, that is the number staff would actually use.
— ASIHub Editorial (official staff account)