μpscaling Optimizes Model Warm Starts
📄#research#pscaling#v1Stalecollected in 20h

μpscaling Optimizes Model Warm Starts

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⚡ 30-Second TL;DR

What changed

General upscaling method

Why it matters

Speeds up training of large models, improving efficiency for diverse inference budgets. Enables practical knowledge transfer from small to large models.

What to do next

Prioritize whether this update affects your current workflow this week.

Who should care:Researchers & Academics

Proposes principled upscaling for model widths inspired by μP, with theory guaranteeing equivalence to widened versions. Extends μTransfer for hyperparameter scaling, avoiding costly retuning at larger sizes. Applicable to diverse architectures and optimizers with infinite-width analysis.

Key Points

  • 1.General upscaling method
  • 2.Hyperparameter transfer technique
  • 3.Theoretical infinite-width guarantees

Impact Analysis

Speeds up training of large models, improving efficiency for diverse inference budgets. Enables practical knowledge transfer from small to large models.

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Original source: ArXiv AI