Empirical Insights into μ-Transfer for Hyperparameter Scaling
Introduction
Large neural network models in natural language processing and computer vision often face challenges with initialization and learning rates, leading to inconsistencies across studies and model sizes. The µ-Parameterization (µP) offers scaling rules for these parameters, enabling zero-shot hyperparameter transfer from small to large models. However, its widespread adoption is hindered by implementation complexity and unresolved questions about its effectiveness at large scales.
Key Findings
Recent research has examined the effectiveness of µP for large-scale transformers, focusing on width and hyperparameter transfer. While some concerns about stability and scalability have been raised, the µ-Transfer approach has shown promise in predicting optimal learning rates for significantly larger models. The research also highlights the impact of trainable gain parameters and attention scales on the transferability of learning rates.
Conclusion
Overall, µP has demonstrated success in transferring learning rates in various scenarios, with the µ-Transfer approach outperforming standard parameterization for transformers. These findings contribute to hyperparameter transfer research, inspiring further exploration in the field.
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