Generative flow networks (GFlowNets) are amortized variational inference algorithms that are trained to sample from unnormalized target distributions over compositional objects. A key limitation of GFlowNets until this time has been that they are restricted to discrete spaces. We present a theory for generalized GFlowNets, which encompasses both existing discrete GFlowNets and ones with continuous or hybrid state spaces, and perform experiments with two goals in mind. First, we illustrate critical points of the theory and the importance of various assumptions. Second, we empirically demonstrate how observations about discrete GFlowNets transfer to the continuous case and show strong results compared to non-GFlowNet baselines on several previously studied tasks. This work greatly widens the perspectives for the application of GFlowNets in probabilistic inference and various modeling settings.

翻译：生成流网络（GFlowNets）是一种摊销变分推理算法，其训练目标是能够从组成性对象上的非归一化目标分布中进行采样。迄今为止，GFlowNets的一个关键局限性在于它们仅限于离散空间。我们提出了一种广义GFlowNets理论，该理论既涵盖了现有的离散GFlowNets，也包含了具有连续或混合状态空间的GFlowNets，并开展了两个目标的实验。首先，我们阐释了该理论的要点以及各种假设的重要性。其次，我们通过实证展示了关于离散GFlowNets的观测结果如何迁移至连续情形，并在多个先前研究的任务上显示出相较于非GFlowNet基线的强劲性能。这项工作极大地拓宽了GFlowNets在概率推理及多种建模场景中的应用前景。