Reading Group (+🧋): Train-to-Test (T²) Scaling Laws

​Join the Snorkel AI Reading Group, a recurring forum to explore the latest frontier developments in AI while building meaningful connections within the community.

In this afternoon's session, Nicholas Roberts will present his recent paper, Train-to-Test (T²) Scaling Laws: Test-Time Scaling Makes Overtraining Compute-Optimal, work that will be featured at COLM 2026 (arXiv preprint available here).

​Agenda:
4 pm - doors open
4:30 pm - talk begins

​🧋🧋🧋 Boba tea and other refreshments will be provided ! 🧋🧋🧋

​Among other things, you'll learn:

• ​Why pretraining scaling laws like Chinchilla don't account for test-time compute, and the trade-off that creates once inference cost scales with model size and sample count.

• ​How Train-to-Test (T²) scaling laws modernize pretraining scaling laws with pass@k modeling, jointly optimizing model size, training tokens, and inference samples under a fixed end-to-end budget.

• ​Why the forecasts hold up across distinct modeling approaches, both the joint scaling effect on task loss and the impact on task accuracy.

• ​Across eight downstream tasks, why optimal pre-training decisions shift radically into the overtraining regime, well outside the range of standard pre-training scaling suites.

• ​How the team validated this by pre-training heavily overtrained models in the region T² forecasts, confirming stronger performance, with the findings holding even after post-training.This work will be featured at COLM 2026, and was covered by VentureBeat: Train-to-test scaling explained.

• ​This work will be featured at COLM 2026, and was covered by VentureBeat: Train-to-test scaling explained.

​This work will be featured at COLM 2026, and was covered by VentureBeat: Train-to-test scaling explained.