Research Paper#Hyperparameter Optimization, Deep Learning, Model Scaling🔬 ResearchAnalyzed: Jan 3, 2026 19:37
Understanding Fast Hyperparameter Transfer in Deep Learning
Published:Dec 28, 2025 04:13
•1 min read
•ArXiv
Analysis
This paper addresses the critical problem of hyperparameter optimization in large-scale deep learning. It investigates the phenomenon of fast hyperparameter transfer, where optimal hyperparameters found on smaller models can be effectively transferred to larger models. The paper provides a theoretical framework for understanding this transfer, connecting it to computational efficiency. It also explores the mechanisms behind fast transfer, particularly in the context of Maximal Update Parameterization ($μ$P), and provides empirical evidence to support its hypotheses. The work is significant because it offers insights into how to efficiently optimize large models, a key challenge in modern deep learning.
Key Takeaways
- •Introduces a framework for understanding hyperparameter transfer across scales.
- •Connects fast transfer to computational efficiency.
- •Investigates the mechanisms behind fast transfer, particularly with $μ$P.
- •Provides empirical evidence supporting the hypothesis of width-stable and width-sensitive components in loss reduction.
Reference
“Fast transfer is equivalent to useful transfer for compute-optimal grid search, meaning that transfer is asymptotically more compute-efficient than direct tuning.”