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name: scaling-analysis description: Run scaling experiments to understand model/data/compute relationships. Use when investigating scaling laws, compute-optimal training, or model size decisions.

Scaling Law Investigation

Empirically test how performance scales with model size, data, or compute.

Step 1: Define Scaling Dimension

Ask what to scale: model size, dataset size, compute, or multiple dimensions

Step 2: Design Scale Points

Use at least 4 points spanning 1-2 orders of magnitude Log-space for data/compute scaling

Step 3: Configure Runs

Same optimizer settings, same data order, same evaluation set across all points

Step 4: Execute Runs

Use /train skill for each run, collect final validation loss, training curve, compute used

Step 5: Analyze Results

• Plot scaling curve with power law fit
• Calculate scaling exponent
• Compare to literature (Kaplan alpha0.076, Chinchilla alpha0.34)

Step 6: Report

Fitted scaling law, comparison to literature, extrapolation predictions, recommendations