Across 600 runs from 10^15 to 10^19 FLOPs, behavioral models show a 2% embedder is compute-optimal at all scales, training is data-heavy at low compute, and optimal negatives increase with budget until memory-limited.
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Scaling Laws for Behavioral Foundation Models over User Event Sequences
Across 600 runs from 10^15 to 10^19 FLOPs, behavioral models show a 2% embedder is compute-optimal at all scales, training is data-heavy at low compute, and optimal negatives increase with budget until memory-limited.