New mech interp hypothesis: neural networks generalize from the very first training step
burny_tech · x · 2026-10-16
Sharing a new interpretability result, zeeshanp highlights the hypothesis that neural networks begin generalizing from the first training step — contradicting the widespread frontier-lab assumption that large networks memorize data first and generalize later. He argues mech interp may now be mature enough to rigorously audit long-held assumptions about training dynamics.
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