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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Sep 12·5 min read
Quantifying the Memorization-to-Generalization Transition: Scaling Laws and Phase Structure in Grokking
TL;DR
A study quantifies the transition from memorization to generalization in neural networks, revealing that data complexity is the primary factor influencing this shift. The findings provide a predictive framework for optimizing model training configurations.
✦ Why It Matters
Optimize your neural network training by focusing on increasing data complexity rather than just model capacity.
Key Takeaways
- Focus on increasing training data to enhance generalization speed.
- Monitor weight decay to identify grokking configurations.
- Utilize two-hidden-layer MLPs for modular arithmetic tasks.
- Apply findings to optimize hyperparameter settings in neural networks.
How It Works
The study employs a power-law scaling relation to quantify the transition from memorization to generalization in neural networks. By analyzing various hyperparameters, it identifies that data complexity significantly influences the timing of this transition, more so than model capacity.
The research also tracks weight norm trajectories, revealing that simpler solutions are favored during the grokking process.