Model collapse
Sources are over 18 months old.
Publication dates and source age
Sources counted: 2
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 tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.
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In plain language
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. [1] [2]
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Limits & distinctions
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. [1] [2]
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A fuller explanation
In studied training loops, each model supplies examples for the next. Errors can accumulate, and uncommon patterns can disappear from the later models' output. [1]
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How it relates to the map
Map context: this qualifies claims about generated data sustaining future model training. [1] [2]
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Sources and what we read
1. AI models collapse when trained on recursively generated data
Source is over 18 months old.
Publication dates and source age
Sources counted: 1
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 tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.
Read definition and recursive-training setup. Checked the 2025 correction to a mathematical symbol. Findings are not evidence that every synthetic-data method fails.
2. Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data
Source is over 18 months old.
Publication dates and source age
Sources counted: 1
Newest dated source: 2024-04-01
Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.
Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.
Read abstract, introduction and language-model setup. Accumulating original and generated examples avoided collapse in tested settings. The original TinyStories text was itself synthetic.
Edition and machine-readable evidence
Content version 0.20.0. Evidence cutoff 2026-09-15; this does not mean every source was read on that day.
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