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）是一种摊销变分推断算法，旨在从定义于组合对象上的未归一化目标分布中进行采样。此前，GFlowNets的一个关键局限在于其仅适用于离散空间。我们提出了广义GFlowNets的理论框架，该框架既涵盖了现有的离散GFlowNets，也适用于具有连续或混合状态空间的GFlowNets，并围绕两个目标开展实验。首先，我们阐释了理论的关键要点及各项假设的重要性。其次，我们通过实证展示了离散GFlowNets的观测结果如何迁移至连续情形，并在多个先前研究任务中，与非GFlowNet基线相比取得了优异表现。本研究极大地拓展了GFlowNets在概率推断及各类建模场景中的应用前景。