Generative Flow Networks (GFlowNets, GFNs) are a generative framework for
learning unnormalized probability mass functions over discrete spaces. Since
their inception, GFlowNets have proven to be useful for learning generative
models in applications where the majority of the discrete space is unvisited
during training. This has inspired some to hypothesize that GFlowNets, when
paired with deep neural networks (DNNs), have favourable generalization
properties. In this work, we empirically verify some of the hypothesized
mechanisms of generalization of GFlowNets. In particular, we find that the
functions that GFlowNets learn to approximate have an implicit underlying
structure which facilitate generalization. We also find that GFlowNets are
sensitive to being trained offline and off-policy; however, the reward
implicitly learned by GFlowNets is robust to changes in the training
distribution.

生成流网络（GFlowNets，GFNs）是一种生成式框架，用于学习离散空间上的非归一化概率质量函数。我们通过实证验证了 GFlowNets 的一些泛化机制假设，发现它们学习逼近的函数具有隐含的结构，有利于泛化。同时，我们还发现 GFlowNets 对离线和离策略训练非常敏感，但是 GFlowNets 隐含学习到的奖励对训练分布的变化具有鲁棒性。