Renormalization Group Theory Explains Emergence and Generalization in LLMs
burny_tech · x · 2026-08-01
Researchers argue that the fundamental reason neural networks generalize—rather than simply memorizing training data—lies in renormalization group (RG) theory and the scale-invariant correlation structure of natural data.
A related paper notes that RG theory satisfies all core criteria for model "emergence":
- Scaling: As generic components scale, new internal organizations arise within the network.
- Criticality: Systems undergo rapid, phase transition-like changes when the layer parameter alpha equals 2.
- Compression: The model exploits internal compressed representations to achieve generalization.
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