# Model collapse

Record: term-model-collapse · Type: term · Edition: 0.20.0 · Evidence cutoff: 2026-09-15

[Read in the atlas](https://theaiatlas.org/ideas/model-collapse/) · [Complete evidence](https://theaiatlas.org/evidence.html#idea-model-collapse) · [JSON](https://theaiatlas.org/records/term-model-collapse.json) · [Pinned complete dataset](https://theaiatlas.org/editions/e74392d479c0e7da8636a7d6a0454d03510df86ffca9931eb86babd952665ca5/data.json)

Dataset pointer: `/glossary/140`. Reviewed: 2026-09-15.

> This is a curated, AI-assisted editorial atlas, not a census, affiliation classifier or independently fact-checked authority.

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## Publication dates and source age

Sources are over 18 months old.

Newest dated source: 2024-07-24. Assessed at this edition’s evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.

Publication age does not establish validity or a new source-reading date. Unknown dates and month/year precision remain explicit in the JSON record.

## /summary

Repeatedly training on earlier models' output can erase patterns from the original data. Other experiments avoided this degradation by retaining the original dataset while adding generated examples.

Claim: claim-term-model-collapse-1a5192ba3d7825ca26b24a72. Annotation: synthesis.

[model-collapse-nature](https://www.nature.com/articles/s41586-024-07566-y) · [model-collapse-accumulation](https://arxiv.org/abs/2404.01413)

## /definition

In studied training loops, each model supplies examples for the next. Errors can accumulate, and uncommon patterns can disappear from the later models' output.

Claim: claim-term-model-collapse-1e4c26398ee834b2e16dc7b8. Annotation: synthesis.

[model-collapse-nature](https://www.nature.com/articles/s41586-024-07566-y)

## /placement

Map context: this qualifies claims about generated data sustaining future model training.

Claim: claim-term-model-collapse-25f52a21ad9f2e54a62ed1b2. Annotation: editorial.

[model-collapse-nature](https://www.nature.com/articles/s41586-024-07566-y) · [model-collapse-accumulation](https://arxiv.org/abs/2404.01413)

## /distinction

The result depends on how datasets are replaced or accumulated. It does not show that all synthetic data is harmful, or diagnose a chatbot's bad answer without examining its training.

Claim: claim-term-model-collapse-09422ce4d5c74cb753a9bb98. Annotation: synthesis.

[model-collapse-nature](https://www.nature.com/articles/s41586-024-07566-y) · [model-collapse-accumulation](https://arxiv.org/abs/2404.01413)

## Source provenance

### model-collapse-nature

[AI models collapse when trained on recursively generated data](https://www.nature.com/articles/s41586-024-07566-y)

Ilia Shumailov and coauthors / Nature · First-hand source (primary) · Published: 2024-07-24 · Updated: 2025-03-21 · Material last read: 2026-09-15 · Verification: read

Read scope is described in the source note.

Read definition and recursive-training setup. Checked the 2025 correction to a mathematical symbol. Findings are not evidence that every synthetic-data method fails.

No archive check recorded.

### model-collapse-accumulation

[Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data](https://arxiv.org/abs/2404.01413)

Matthias Gerstgrasser and coauthors / arXiv · First-hand source (primary) · Published: 2024-04-01 · Updated: 2024-04-29 · Material last read: 2026-09-15 · Verification: read

Read scope is described in the source note.

Read abstract, introduction and language-model setup. Accumulating original and generated examples avoided collapse in tested settings. The original TinyStories text was itself synthetic.

No archive check recorded.