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This paper establishes the theoretical equivalence between Generative Marginalization Models and Generative Flow Networks, introducing "Particle GFlowNets" to accelerate training in large combinatorial spaces. By extending these models to non-autoregressive processes and incorporating an automatic rejuvenation criterion based on the Gelman-Rubin statistic, the authors significantly improve convergence speeds for discrete distribution sampling. The work represents a notable advancement in the mathematical foundations of generative modeling and inference algorithms.
Particle GFlowNets: Rethinking Generative Marginalization Models
arXiv:2609.11538v1 Announce Type: new Abstract: Generative Marginalization Models (MaMs) have been recently introduced as efficient neural sampling models for any-order autoregressive modelling of discrete distributions. By learning both the marginal and conditional probabilities of a persistent-block Gibbs sampler, MaMs enable fast posterior evaluation with a single neural network forward pass. While prior work has considered MaMs to be distinct from Generative Flow Networks (GFlowNets), a well-established paradigm for inference in discrete stochastic models, we show that they are equivalent. Then, we also extend MaMs' sampling strategy to non-autoregressive generative processes. In particular, we describe an automatic criterion for full-state rejuvenation of the Gibbs sampler, derived from the Gelman-Rubin statistic, which plays a key role in speeding up learning convergence. Our experiments show that our method, called Particle GFlowNets, markedly accelerates training in large combinatorial spaces.
Abstract:Generative Marginalization Models (MaMs) have been recently introduced as efficient neural sampling models for any-order autoregressive modelling of discrete distributions. By learning both the marginal and conditional probabilities of a persistent-block Gibbs sampler, MaMs enable fast posterior evaluation with a single neural network forward pass. While prior work has considered MaMs to be distinct from Generative Flow Networks (GFlowNets), a well-established paradigm for inference in discrete stochastic models, we show that they are equivalent. Then, we also extend MaMs' sampling strategy to non-autoregressive generative processes. In particular, we describe an automatic criterion for full-state rejuvenation of the Gibbs sampler, derived from the Gelman-Rubin statistic, which plays a key role in speeding up learning convergence. Our experiments show that our method, called Particle GFlowNets, markedly accelerates training in large combinatorial spaces.
Machine Learning (cs.LG)
arXiv:2609.11538 [cs.LG]
(or arXiv:2609.11538v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.11538 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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