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METR · July 21, 2026

Expenditure Horizon: Measuring Optimization Ability, with an Application to NanoGPT

framework Model EvaluationAI EngineeringAgent Security
Why it matters

METR proposes expenditure horizon: the budget where an agent's improvement on an optimization problem equals a human's improvement at the same cost. Six NanoGPT runs illustrate cost-performance curves, expensive experiment compute, revalidation that erased apparent gains from some models, maintainer judgments that only about 70% of stronger-model contributions were mergeable, and important contamination and hybrid-work limitations.

My takeaway: For optimization-agent evaluations, report the complete cost-performance curve, include token, experiment, and human-review costs, independently re-run the best artifacts, and have domain maintainers judge mergeability. Disclose harness inefficiency, task contamination, noisy metrics, and the gap between autonomous and human-agent performance before claiming R&D acceleration.
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