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464 Train-to-Test scaling explained: How to optimize your end-to-end AI compute budget for inference

VentureBeat
VentureBeat 1 place · 04/17/2026 13:34 EDT

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

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