# Train-to-Test (T2) Scaling Laws **Source:** https://www.startuphub.ai/startups/train-to-test-t2-scaling-laws **Profile type:** Startup > A framework for jointly optimizing LLM training and inference costs. ## About Train-to-Test (T2) scaling laws is a research framework developed by academics at the University of Wisconsin-Madison and Stanford University. It addresses the disconnect between traditional LLM pretraining scaling laws (optimizing for training cost) and real-world inference needs (where techniques like repeated sampling increase accuracy but also cost). T2 jointly optimizes a model's parameter size, training data volume, and the number of test-time inference samples to achieve compute-optimal performance for reasoning-heavy applications. ## Key facts - **Slug:** train-to-test-t2-scaling-laws - **Location:** Stanford, California, USA - **Operating status:** Active - **Sectors / tags:** Artificial Intelligence, Machine Learning, Computer Science ## Frequently asked ### What does Train-to-Test (T2) Scaling Laws do? Train-to-Test (T2) scaling laws is a research framework developed by academics at the University of Wisconsin-Madison and Stanford University. It addresses the disconnect between traditional LLM pretraining scaling laws (optimizing for training cost) and real-world inference needs (where techniques like repeated sampling increase accuracy but also cost). T2 jointly optimizes a model's parameter size, training data volume, and the number of test-time inference samples to achieve compute-optimal p… ### Where is Train-to-Test (T2) Scaling Laws headquartered? Train-to-Test (T2) Scaling Laws is headquartered in Stanford, California, USA. ### What industry does Train-to-Test (T2) Scaling Laws operate in? Train-to-Test (T2) Scaling Laws operates in Artificial Intelligence, Machine Learning, Computer Science. ## JSON-LD Structured data aligned with this page's `