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Signal from Structure: Exploiting Submodular Upper Bounds in Generative Flow Networks
Generative Flow Networks (GFlowNets; GFNs) are a class of generative models that learn to sample compositional objects proportional to their a priori unknown value, their reward. We focus on the case where the reward has a specified, actionable structure, namely that it is submodular. We show submod
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