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Short Answer

In the training of GPT-4, what was the smallest compute budget—expressed as a fraction of the full budget—used on smaller models to accurately predict aspects of final performance?

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Updated 2026-09-11

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Prep Sessions

Transformer Architecture and Large Language Model Capabilities @ University of Michigan - Ann Arbor

Ch.2 Model Scaling and Capability Evaluation - Transformer Architecture and Large Language Model Capabilities @ University of Michigan - Ann Arbor

Predictable Scaling and Compute Laws - Transformer Architecture and Large Language Model Capabilities @ University of Michigan - Ann Arbor

Frontier Model Dynamics: Scaling Laws, Calibration, and Post-Training Alignment @ University of Michigan - Ann Arbor

Ch.1 Scaling Dynamics - Frontier Model Dynamics: Scaling Laws, Calibration, and Post-Training Alignment @ University of Michigan - Ann Arbor

Predictable Scaling Laws and Performance Extrapolation - Frontier Model Dynamics: Scaling Laws, Calibration, and Post-Training Alignment @ University of Michigan - Ann Arbor

Frontier Foundation Models, Capability Evaluation, and Just-In-Time Agent Harnesses @ University of Michigan - Ann Arbor

Ch.1 Foundation Model Capabilities and Benchmarking - Frontier Foundation Models, Capability Evaluation, and Just-In-Time Agent Harnesses @ University of Michigan - Ann Arbor

Predictable Scaling Laws in Foundation Models - Frontier Foundation Models, Capability Evaluation, and Just-In-Time Agent Harnesses @ University of Michigan - Ann Arbor