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GFlowNets can be harnessed to learn optimal transport plans, bridging the gap between generative modeling and optimal transport theory.
GFlowNets can provably learn shortest paths in arbitrary graphs by minimizing total flow, outperforming specialized methods on Rubik's Cube with less search.
