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Julian Enoch

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#machine learning Preprint Oct 2026

Exact Scaling Laws for a Random-Features Bigram Model

This work study capacity-limited scaling in a bottlenecked bigram model with power-law (Zipf) token statistics, viewed as an analytically tractable toy model of a large language model, and suggests that heavy-tailed language statistics may underlie both optimization- and capacity-driven scaling.

Julian Enoch, Elvis Dohmatob · 0 citations