The 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 models on vastly more data than traditional rules prescribe, and then use the saved computational overhead to generate multiple repeated samples at inference.For enterprise AI application developers who are training their own models, this research provides a proven blueprint for maximizing return on investment. It shows that AI reasoning does not necessarily require spending huge amounts on frontier models. Instead, smaller models can yield stronger performance on complex tasks while keeping per-query inference costs manageable within real-world deployment budgets.Conflicting scaling lawsScaling laws are an important part of developing large language models. Pretraining scaling laws dictate the best way to allocate compute during the model’s creation, while test-time scaling laws guide how to allocate compute during deployment, such as letting the model “think longer” or generating multiple reasoning samples to solve complex problems.The problem is that these scaling laws have been developed completely independently of one another despite being fundamentally intertwined.A model’s parameter size and training duration directly dictate both the quality and the per-query cost of its inference samples. Currently, the industry gold standard for pretraining is the Chinchilla rule, which suggests a compute-optimal ratio of roughly 20 training tokens f …
