June 5, 2024, 4:42 a.m. | Chunhui Li, Cheng-Hao Liu, Dianbo Liu, Qingpeng Cai, Ling Pan

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.01901v1 Announce Type: new
Abstract: Generative Flow Networks (GFlowNets), a new family of probabilistic samplers, have recently emerged as a promising framework for learning stochastic policies that generate high-quality and diverse objects proportionally to their rewards. However, existing GFlowNets often suffer from low data efficiency due to the direct parameterization of edge flows or reliance on backward policies that may struggle to scale up to large action spaces. In this paper, we introduce Bifurcated GFlowNets (BN), a novel approach that …

abstract arxiv cs.lg data diverse edge efficiency family flow framework generate generative however low networks objects policies quality reliance stochastic type

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