{
  "@context": "https://schema.org",
  "@type": "TechArticle",
  "@id": "https://anchorfact.org/kb/synthetic-data-training",
  "headline": "Synthetic Data in AI Training",
  "description": "Synthetic data — generating training examples from other AI models — has emerged as both a powerful scaling technique and a fundamental risk. Models like Phi-4 achieve state-of-the-art results primarily from synthetic data, while model collapse threatens recursive use.",
  "dateCreated": "2026-05-24T02:49:13.664Z",
  "dateModified": "2026-05-24",
  "author": {
    "@type": "Organization",
    "name": "AnchorFact"
  },
  "publisher": {
    "@type": "Organization",
    "name": "AnchorFact",
    "url": "https://anchorfact.org"
  },
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "anchorfact:confidence": "high",
  "anchorfact:generationMethod": "ai_assisted",
  "citation": [
    {
      "@type": "CreativeWork",
      "name": "Phi-4 Technical Report",
      "sameAs": "https://arxiv.org/abs/2412.08905"
    },
    {
      "@type": "CreativeWork",
      "name": "AI models collapse when trained on recursively generated data",
      "sameAs": "https://www.nature.com/articles/s41586-024-07566-y"
    }
  ]
}