{
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  "@type": "article",
  "@id": "https://anchorfact.org/kb/meta-learning",
  "headline": "Meta-Learning: Learning to Learn with MAML and Reptile",
  "description": "Meta-learning trains models to learn efficiently. Given a distribution of tasks, the meta-learner acquires knowledge that accelerates learning on new tasks — the model \"learns how to learn.\" MAML finds optimal initializations; Reptile simplifies the process.",
  "dateCreated": "2026-05-24T02:49:13.634Z",
  "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": "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks (MAML)",
      "sameAs": "https://arxiv.org/abs/1703.03400"
    },
    {
      "@type": "CreativeWork",
      "name": "On First-Order Meta-Learning Algorithms (Reptile)",
      "sameAs": "https://arxiv.org/abs/1803.02999"
    }
  ]
}