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muTransfer: small-model hyperparameter tuning across width

Used maximal-update parameterization to tune optimization/initialization settings on smaller neural networks and transfer them to larger models without direct full-scale tuning.

What is recorded here?

Used maximal-update parameterization to tune optimization/initialization settings on smaller neural networks and transfer them to larger models without direct full-scale tuning.

Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.

Scientific scope

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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People and systems

Greg Yang; Edward J. Hu; Igor Babuschkin; Szymon Sidor; Xiaodong Liu; David Farhi; Nick Ryder; Jakub Pachocki; Weizhu Chen; Jianfeng Gao

Source-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.

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English catalogue narrative

muTransfer: small-model hyperparameter tuning across width

Used maximal-update parameterization to tune optimization/initialization settings on smaller neural networks and transfer them to larger models without direct full-scale tuning.

Scientific scope

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.

Reading status

This 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.

People and systems

Greg Yang; Edward J. Hu; Igor Babuschkin; Szymon Sidor; Xiaodong Liu; David Farhi; Nick Ryder; Jakub Pachocki; Weizhu Chen; Jianfeng Gao

Source-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.

Original sources