Particle GFlowNets: Rethinking Generative Marginalization Models

• URL: http://arxiv.org/abs/2609.11538v1
• Date: Thu, 10 Sep 2026 13:37:28 GMT
• Title: Particle GFlowNets: Rethinking Generative Marginalization Models
• Abstract summary: Gene Marginalization Models (MaMs) are efficient neural sampling models for any-order autoregressive modelling of discrete distributions.<n>We extend MaMs' sampling strategy to non-autoregressive generative processes.<n>Our method, called Particle GFlowNets, markedly accelerates training in large spaces.
• Score: 15.290146072002317
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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