Baking Symmetry into GFlowNets

• URL: http://arxiv.org/abs/2406.05426v1
• Date: Sat, 8 Jun 2024 10:11:10 GMT
• Title: Baking Symmetry into GFlowNets
• Authors: George Ma, Emmanuel Bengio, Yoshua Bengio, Dinghuai Zhang,
• Abstract summary: GFlowNets have exhibited promising performance in generating diverse candidates with high rewards. This study aims to integrate symmetries into GFlowNets by identifying equivalent actions during the generation process.
• Score: 58.932776403471635
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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