Train-to-Test ($T^2$) Scaling Laws: Optimizing LLM Training and Inference
University of Wisconsin-Madison and Stanford researchers have introduced Train-to-Test (T²) scaling laws, a framework that jointly optimizes model size, training data volume and inference-time sampling count to minimize total compute ... Read More