IRSL applies IRT to reduce scaling law estimation from O(M×N) to O(M+N) parameters, enabling reliable estimates with only 50 questions per benchmark after calibration and generalizable ability scores across related benchmarks.
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Maps common low-compute research strategies for foundation models onto statistical, internal, external, and construct validity threats via a causal-inference lens.
Case study applies frozen staged budgets and promotion rules to twelve micro-pretraining configurations, identifying a top bridge condition at 12 hours with 169 GPU-hours total versus higher counterfactual costs.
citing papers explorer
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Item Response Scaling Laws: A Measurement Theory Approach for Efficient and Generalizable Neural Scaling Estimation
IRSL applies IRT to reduce scaling law estimation from O(M×N) to O(M+N) parameters, enabling reliable estimates with only 50 questions per benchmark after calibration and generalizable ability scores across related benchmarks.
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Validity Threats for Foundation Model Research
Maps common low-compute research strategies for foundation models onto statistical, internal, external, and construct validity threats via a causal-inference lens.