Toward Alignment Scaling Laws: A Framework and First Preregistered Measurements
Quick summary
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_r1. We give three operationalizations of burden and distinguish observed, audited and true alignment. A toy model, in which corrections consume capability headr
Key takeaways
- 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_r1.
- We give three operationalizations of burden and distinguish observed, audited and true alignment.
Why it matters
“Toward Alignment Scaling Laws: A Framework and First Preregistered Measurements” shows why AI risk cannot be reduced to answer accuracy. Access controls, logging, human approval and incident response need to be designed into the workflow from the start.
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