We tackle the problem of sampling from intractable high-dimensional density
functions, a fundamental task that often appears in machine learning and
statistics. We extend recent sampling-based approaches that leverage controlled
stochastic processes to model approximate samples from these target densities.
The main drawback of these approaches is that the training objective requires
full trajectories to compute, resulting in sluggish credit assignment issues
due to use of entire trajectories and a learning signal present only at the
terminal time. In this work, we present Diffusion Generative Flow Samplers
(DGFS), a sampling-based framework where the learning process can be tractably
broken down into short partial trajectory segments, via parameterizing an
additional "flow function". Our method takes inspiration from the theory
developed for generative flow networks (GFlowNets), allowing us to make use of
intermediate learning signals and benefit from off-policy exploration
capabilities. Through a variety of challenging experiments, we demonstrate that
DGFS results in more accurate estimates of the normalization constant than
closely-related prior methods.

我们提出了一种基于扩散生成流采样器 (DGFS) 的抽样框架，该方法可以将学习过程易于处理地分解为短的部分轨迹段，通过参数化额外的 “流函数”，并且在各种具有挑战性的实验中展示了相比于相关先前方法更准确的规范常量估计。