arXiv:2606.04405v1 Announce Type: new Abstract: Modern Transformer architectures frequently employ normalization mechanisms such as RMSNorm and Query-Key Normalization, making parts of the model approximately scale-invariant with respect to weight magnitudes. In this regime, standard Frobenius-norm weight decay acts purely along the radial direction of the weight space and cannot directly simplify the function represented by the normalized layer. We study grokking in small algorithmic tasks through this lens and propose \emph{Low-Rank Decay} (LRD), a nuclear-norm-like spectral regularizer whos
The continuous evolution of Transformer architectures and the increasing complexity of AI models necessitate more effective regularization techniques to improve learning efficiency and mitigate issues like grokking.
Improving the architectural foundations and learning stability of AI models is crucial for advancing AI capabilities and developing more robust and predictable AI systems.
New regularization methods like Low-Rank Decay offer a more nuanced approach to weight decay in scale-invariant transformers, potentially leading to more efficient and stable AI training.
- · AI researchers and developers
- · Companies building large-scale AI models
- · Sectors reliant on robust AI performance
- · Developers using less optimized regularization methods
The adoption of Low-Rank Decay could lead to faster convergence and better generalization in Transformer models.
Improved model training efficiency might accelerate the development and deployment of more sophisticated AI applications.
More robust AI models could reduce deployment risks and foster greater public trust in advanced AI systems.
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Read at arXiv cs.LG