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arXiv — cs.AI preprintsInternational7 October 2026

Toward Alignment Scaling Laws: A Framework and First Preregistered Measurements

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arXiv:2610.08540v1 Announce Type: new Abstract: Whether alignment gets easier or harder as models grow is often argued from isolated findings, as if alignment were one property. We treat it as a family of measurable scaling relations: for each risk category r, the alignment burden needed to hold a fixed safety target is modeled as B_r(N)=a_rN^alpha_r, with N a capability proxy; against a budget proportional to N, scaling helps if alpha_r 1. We give three operationalizations of burden and distinguish observed, audited and true alignment. A toy model, in which corrections consume capability head
— arXiv — cs.AI preprints

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