arXiv:2605.22297v1 Announce Type: new Abstract: Learning rate configuration is a fundamental aspect of modern deep learning. The prevailing practice of applying a uniform learning rate across all layers overlooks the structural heterogeneity of Transformers, potentially limiting their effectiveness as the backbone of Large Language Models (LLMs). In this paper, we introduce Layerwise Learning Rate (LLR), an adaptive scheme that assigns distinct learning rates to individual Transformer layers. Our method is grounded in Heavy-Tailed Self-Regularization (HT-SR) theory, which characterizes the emp
This research is emerging now as LLMs become foundational infrastructure, prompting a deeper investigation into their fundamental training mechanics to optimize performance and efficiency.
Improving LLM training efficiency and effectiveness through adaptive learning rates can significantly impact the development cost and capabilities of future AI models, affecting accessibility and deployment.
The conventional practice of uniform learning rates is being challenged by a more nuanced, layer-specific approach, suggesting a shift in how LLMs are optimized and potentially leading to more powerful or efficient models.
- · AI researchers
- · LLM developers
- · Cloud AI providers
- · Fixed learning rate methodologies
- · Less optimized LLM development practices
Individual layers within LLMs will be trained with distinct learning rates tailored to their specific needs.
This optimization could lead to faster training times or improved performance metrics for LLMs, reducing computational resource demands.
More efficient LLM training might democratize access to advanced AI capabilities or enable the development of larger, more complex models that were previously infeasible.
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Read at arXiv cs.LG