Research: Test-Time Compute Makes Overtraining Compute-Optimal
ajratner · x · 2026-08-12
Snorkel AI hosted a reading group to discuss a COLM 2026 paper on "Train-to-Test (T²) Scaling Laws."
Key Insights
- Traditional pretraining scaling laws like Chinchilla do not account for test-time compute.
- The trade-off shifts when inference cost scales with model size and sample count.
- T² scaling laws modernize pretraining laws using pass@k modeling, jointly optimizing model size, training tokens, and inference samples under a fixed end-to-end budget.
- Across eight downstream tasks, optimal pre-training decisions shift radically into the overtraining regime, well outside the range of standard pre-training scaling suites.
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