Train-to-Test (T²) Scaling Laws: Why Reasoning Models Should Be Overtrained

At our latest Snorkel AI Reading Group, Nicholas Roberts presented Test-Time Scaling Makes Overtraining Compute-Optimal, work coauthored with Sungjun Cho, Zhiqi Gao, Tzu-Heng Huang, Albert Wu, Gabriel Orlanski, Avi Trost, Kelly Buchanan, Aws Albarghouthi, and Frederic Sala. Pre-training scaling laws answer a familiar question: given a fixed training budget, how should a team allocate compute between model size and training data? Test-time scaling asks a different question: how much inference compute should a model spend generating and verifying candidate answers? In modern reasoning systems, these decisions are coupled—but they have largely been studied in isolation. The paper introduces Train-to-Test, or T², scaling laws that optimize model size, training tokens, and repeated inference samples together. Across eight downstream tasks, the authors find that once test-time compute is included, the compute-optimal choice shifts sharply toward smaller models trained on far more tokens than standard Chinchilla scaling would recommend. The finding survives post-training and aligns with the broader industry trend toward heavily overtrained small models. Lightly edited for readability. Thank you for the introduction and for having me here today. I’m going to tell you about recent work on combining pre-training scaling laws with test-time reasoning. Here’s the roadmap. First, I’ll give some…