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。

扩散生成流采样器: 通过部分轨迹优化改善学习信号