The Future of Neural Network Training: Empirical Insights into μ-Transfer for Hyperparameter Scaling

Large neural network models dominate natural language processing and computer vision, but their initialization and learning rates often rely on heuristic methods, leading to inconsistency across studies and model sizes. The µ-Parameterization (µP) proposes scaling rules for these parameters, facilitating zero-shot hyperparameter transfer from small to large models. However, despite its potential, widespread adoption of […]

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