Baking Symmetry into GFlowNets
This addresses inefficiencies in GFlowNet training for generating diverse, high-reward candidates, but it is incremental as it builds on existing methods.
The paper tackled the problem of GFlowNets not accounting for isomorphic actions, which reduces training efficiency and output quality, by integrating symmetries to identify equivalent actions, resulting in improved performance on synthetic data.
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.