Investigating Generalization Behaviours of Generative Flow Networks
Lazar Atanackovic Emmanuel Bengio
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.