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        "description": "Inherits muP and tensor-program theory. Does not claim invention of muP or transfer of every hyperparameter: original table excludes regularization such as dropout/weight decay, while depth/batch/sequence/time transfer has empirical caveats. Small-model tuning still costs compute.",
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      "statement": "# muTransfer: small-model hyperparameter tuning across width\n\nUsed maximal-update parameterization to tune optimization/initialization settings on smaller neural networks and transfer them to larger models without direct full-scale tuning.\n\n## Scientific scope\n\nInherits muP and tensor-program theory. Does not claim invention of muP or transfer of every hyperparameter: original table excludes regularization such as dropout/weight decay, while depth/batch/sequence/time transfer has empirical caveats. Small-model tuning still costs compute.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nGreg Yang; Edward J. Hu; Igor Babuschkin; Szymon Sidor; Xiaodong Liu; David Farhi; Nick Ryder; Jakub Pachocki; Weizhu Chen; Jianfeng Gao\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer](https://arxiv.org/abs/2203.03466v1)\n",
      "summary": "Used maximal-update parameterization to tune optimization/initialization settings on smaller neural networks and transfer them to larger models without direct full-scale tuning.",
      "title": "muTransfer: small-model hyperparameter tuning across width"
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