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This research applies Proximal Policy Optimization (PPO) within the Generative Flow Network framework, demonstrating superior convergence speed and data efficiency over standard training objectives for discrete sampling tasks. By bridging theoretical connections between GFlowNets and entropy-regularized reinforcement learning, the study offers a practical advancement in generating complex structures like molecular graphs from synthetic energy benchmarks.
Proximal Policy Optimization for Amortized Discrete Sampling
arXiv:2606.15793v1 Announce Type: new Abstract: This paper explores policy gradient algorithms for training stochastic policies to sample from structured discrete probability distributions under the Generative Flow Network (GFlowNet) framework. Building on extensive theoretical connections between GFlowNets and entropy-regularized reinforcement learning, we derive equivalents of standard policy gradient algorithms for training GFlowNets, as well as experimentally explore their various methodological aspects, including baseline training and advantage estimation. Most importantly, our work is the first to derive and successfully apply proximal policy optimization to GFlowNets, showing its improved convergence speed and data efficiency compared to standard GFlowNet training objectives on benchmarks ranging from synthetic energies to molecular graph generation.
Abstract:This paper explores policy gradient algorithms for training stochastic policies to sample from structured discrete probability distributions under the Generative Flow Network (GFlowNet) framework. Building on extensive theoretical connections between GFlowNets and entropy-regularized reinforcement learning, we derive equivalents of standard policy gradient algorithms for training GFlowNets, as well as experimentally explore their various methodological aspects, including baseline training and advantage estimation. Most importantly, our work is the first to derive and successfully apply proximal policy optimization to GFlowNets, showing its improved convergence speed and data efficiency compared to standard GFlowNet training objectives on benchmarks ranging from synthetic energies to molecular graph generation.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
arXiv:2606.15793 [cs.LG]
(or arXiv:2606.15793v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.15793 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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