{
  "$type": "site.standard.document",
  "contributors": [
    {
      "did": "did:plc:igunvse2uemkwmci3igoxhu5",
      "displayName": "Oz Akan",
      "role": "author"
    }
  ],
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  "description": "Nesting Power and Flexibility into ML Embeddings",
  "path": "/techs/matryoshka-representation-learning",
  "publishedAt": "2025-09-04T21:00:00.000Z",
  "site": "at://did:plc:igunvse2uemkwmci3igoxhu5/site.standard.publication/luminary-blog",
  "tags": [
    "aiml",
    "embeddings"
  ],
  "textContent": "Matryoshka Representation Learning teaches a single model to pack useful signal into the prefix of its embedding. You can then truncate vectors (e.g., 768→256→128→64 dims) to trade tiny drops in quality for big wins in latency, storage, and bandwidth—without retraining separate models.",
  "title": "What is Matryoshka Representation Learning (MRL)?"
}