Bifurcated Generative Flow Networks
arxiv(2024)
摘要
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 employs a bifurcated
architecture to factorize the flows into separate representations for state
flows and edge-based flow allocation. This factorization enables BN to learn
more efficiently from data and better handle large-scale problems while
maintaining the convergence guarantee. Through extensive experiments on
standard evaluation benchmarks, we demonstrate that BN significantly improves
learning efficiency and effectiveness compared to strong baselines.
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