  GFlowNets have exhibited promising performance in generating diverse
candidates with high rewards. These networks generate objects incrementally and
aim to learn a policy that assigns probability of sampling objects in
proportion to rewards. However, the current training pipelines of GFlowNets do
not consider the presence of isomorphic actions, which are actions resulting in
symmetric or isomorphic states. This lack of symmetry increases the amount of
samples required for training GFlowNets and can result in inefficient and
potentially incorrect flow functions. As a consequence, the reward and
diversity of the generated objects decrease. In this study, our objective is to
integrate symmetries into GFlowNets by identifying equivalent actions during
the generation process. Experimental results using synthetic data demonstrate
the promising performance of our proposed approaches.
