arXiv:2604.00316v2 Announce Type: replace-cross Abstract: Grokking occurs when a model achieves high training accuracy but generalization to unseen test points happens long after that. This phenomenon was initially observed on a class of algebraic problems, such as learning modular arithmetic (Power et al., 2022). We study grokking on algebraic tasks in a class of feature learning kernels via the Recursive Feature Machine (RFM) algorithm (Radhakrishnan et al., 2024), which iteratively updates feature matrices through the Average Gradient Outer Product (AGOP) of an estimator in order to learn t
This paper, published on arXiv, details new research into generalization in feature learning kernels specifically addressing the 'grokking' phenomenon.
Understanding the mechanisms behind grokking and generalization is crucial for developing more robust and reliable AI models, especially as AI systems become more autonomous and integrated into critical applications.
This research contributes to the theoretical understanding of how AI models generalize, potentially leading to more efficient training methodologies and predictable performance in real-world scenarios.
- · AI researchers
- · Machine learning theoreticians
- · Developers of AI agents
- · AI development relying solely on empirical trial-and-error
Improved theoretical foundations for AI model generalization will emerge.
More reliable and less 'black box' AI systems, particularly for complex tasks, could be developed.
This could accelerate the deployment of autonomous AI agents in sensitive applications as their behavior becomes more predictable.
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Read at arXiv cs.LG