arXiv:2606.30388v1 Announce Type: cross Abstract: Delayed generalization (\ie~grokking) refers to the phenomenon in which a neural network fits its training data early in training but only begins to generalize after a prolonged delay, often through an abrupt transition. Despite extensive empirical study, its underlying mechanism remains poorly understood. In this work, we first theoretically characterize a shell--core topological configuration of the reachable solution space induced by Adam's optimization dynamics with weight-shrinkage regularization, supported by empirical evidence. This opti
This research provides a theoretical characterization of 'grokking', a known but poorly understood phenomenon in neural network training, refining our understanding of AI optimization dynamics.
Understanding the mechanisms behind grokking can lead to more efficient and reliable AI model development, potentially reducing training times and improving generalization capabilities.
The theoretical framework presented offers new avenues for controlling and predicting the generalization behavior of neural networks, impacting future AI research and development methodologies.
- · AI researchers
- · Machine learning engineers
- · Deep learning framework developers
Improved understanding of neural network training dynamics, specifically the grokking phenomenon.
Development of more stable and predictable AI training algorithms that consistently achieve generalization, reducing trial-and-error.
The acceleration of AI development across industries due to more robust and efficient model creation processes, potentially lowering the computational cost of achieving high-performing models.
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