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arXiv · 2609.11538

Particle GFlowNets: Rethinking Generative Marginalization Models

Tiago da Silva·Diego Mesquita·Salem Lahlou
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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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Tiago da Silva, Diego Mesquita, Salem Lahlou. 2026-09-10. Particle GFlowNets: Rethinking Generative Marginalization Models. https://arxiv.org/abs/2609.11538

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