Efficient and Unbiased Sampling of Boltzmann Distributions via Consistency Models

Understand

Diffusion models have shown promising potential for advancing Boltzmann Generators.

• However, two critical challenges persist: (1) inherent errors in samples due to model imperfections, and (2) the requirement of hundreds of functional evaluations (NFEs) to achieve high-quality samples.
• While existing solutions like importance sampling and distillation address these issues separately, they are often incompatible, as most distillation models lack the necessary density information for importance sampling.
• This paper introduces a novel sampling method that effectively combines Consistency Models (CMs) with importance sampling.