Train-to-Test scaling explained: How to optimize your end-to-end AI compute budget for inference
Ben Dickson 10:34 am, PT, April 17, 2026 Image credit: VentureBeat with Nano Bannana ProThe standard guidelines for building large language models (LLMs) optimize only for training costs and ignore inference costs. This poses a challenge for real-world applications that use inference-time scaling techniques to increase the accuracy of model responses, such as drawing multiple reasoning samples from a model at deployment.To bridge this gap, researchers at University of Wisconsin-Madison and Stanford University have introduced Train-to-Test (T2) scaling laws, a framework that jointly optimizes a model’s parameter size, its training data volume, and the number of test-time inference samples.In practice, their approach proves that it is compute-optimal to train substantially smaller…
