arXiv:2606.13753v1 Announce Type: cross Abstract: Grokking is the delayed onset of generalization in neural networks, arising long after they fit the training data. Whether the weight norm causes this delay is disputed: some studies report a critical norm at the transition, others observe grokking with no fixed norm at all. We settle this by intervening on the norm during training rather than only observing it. Under free training with weight decay, networks grok when the weight norm reaches a value Wc that varies little across seeds and learning rates (CV 1 to 2 percent) and grows with the mo
This paper offers a clearer understanding of grokking, a specific neural network phenomenon, by providing a causal explanation for its timing and dependence on weight norm, moving beyond observational studies.
Understanding the mechanisms behind grokking can lead to more efficient and predictable training of large neural networks, impacting the development and deployment of advanced AI models.
The established causal link between weight norm and the grokking timescale suggests specific interventions during training could control generalization, refining how AI models are optimized.
- · AI researchers
- · Machine learning model developers
- · Companies developing foundation models
- · Ad-hoc AI model optimization methods
More robust and controlled generalization in neural networks could become achievable through targeted weight norm management.
This improved understanding might accelerate the development of more complex and performant AI systems with less trial-and-error.
The insights could contribute to the broader goal of explainable AI, enhancing trust and accelerating adoption in critical applications.
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Read at arXiv cs.AI