An article explains speculative sampling, a technique used to improve the efficiency of sampling in machine learning. The core idea is to use a draft sampling distribution to achieve the same results as a target sampling distribution. The article details how to manage over-sampled and under-sampled tokens.

This approach aims to accelerate model inference.