Raschka's Inference Scaling Part 1: Sampling for Better Accuracy

Sebastian Raschka starts a series on inference scaling by modifying text generation with temperature scaling, top-p filtering, and multinomial sampling to produce diverse outputs.

He says this enables self-consistency and best-of-N approaches that improve answer accuracy by more than 2x. The video covers chain-of-thought prompting, a MATH-500 evaluation, and accuracy versus compute tradeoffs.