Model Collapse
What happens when models train on their own output for generations — a real effect, and the version you've heard depends on an assumption nobody makes in practice.
Reviewed July 14, 2026Stable
When not to use it
- As an argument against synthetic data generally. Some of the field's best recent results — phi, distillation, RLVR pipelines — depend on it, under accumulation and filtering.
- As a prediction about the open web. The mechanism needs replacement; the web accumulates, and the honest answer at internet scale is that nobody has measured it.
- To explain a model that's simply undertrained. Blandness has many causes, and collapse is a specific distributional claim you can test for.
Reach for something else instead
- Data filtering and dedup address the real risk with none of the drama, and are what mature pipelines actually do.
- Accumulation — keep the real data, add synthetic — is the finding that matters, and it's a one-line policy.
- Verification — RLVR-style checkable signals — sidesteps the problem where a verifier exists, because you're not learning from output, you're learning from correctness.
Read more on the blog
- Why AI models get worse: forgetting, collapse, and driftThree different mechanisms quietly degrade AI systems, catastrophic forgetting, model collapse, and data drift. They get blamed for each other constantly. Here's how to tell them apart, and which one is actually eating your accuracy.
- What is synthetic data? Training AI on AI-made dataFaced with running out of human text to train on, AI labs increasingly train models on data the models generate themselves. This is synthetic data, and it comes with a famous warning called model collapse. The resolution of that tension, that verification and curation are what separate collapse from improvement, is the whole story and one of the most important ideas in how modern AI is built.
Further reading
- Shumailov et al. (2024), AI models collapse when trained on recursively generated data — the Nature paper; collapse under the replace regime.
- Gerstgrasser et al. (2024), Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data — the same question under accumulation; test error plateaus rather than diverging.
- Shumailov et al. (2023), The Curse of Recursion: Training on Generated Data Makes Models Forget — the earlier arXiv statement of the mechanism.
Primary sources, listed so you can check the claims on this page rather than take them on trust.
Where people go wrong
- Citing the Nature result without the regime. Collapse follows from replacing data each generation; under accumulation, the same experiment plateaus.
- Reading it as an argument for avoiding synthetic data. The finding argues for keeping your real data, which is a different instruction entirely.
- Assuming it's visible by reading outputs. The degradation is distributional — narrowed tails, converged lengths — and looks like fluent, confident prose right up until it matters.
At a glance
FieldMachine Learning
Mechanismtails truncate, errors compound
Requiresreplacing real data with synthetic
Under accumulationlargely doesn't happen
DifficultyIntermediate
Where this sits
A destination. 2 concepts lead here, and nothing in the corpus depends on it.
2Levelsteps in
2Needs firstconcepts
0Opens upnothing further
1Areastays here
Learn these firstSynthetic Data
Computed from the prerequisite graph, not assigned. How this works