Particle GFlowNets: Rethinking Generative Marginalization Models — Paper

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.

Authors 3

Tiago da Silva, Diego Mesquita, Salem Lahlou

Specification

Official page: arxiv.org/abs/2609.11538
Source:arXiv (Atom API + RSS)T1observed 3 h agohigh
Arxiv announce type: new
Source:arXiv (Atom API + RSS)T1observed 3 h agohigh
arXiv id: 2609.11538
Source:arXiv (Atom API + RSS)T1observed 3 h agohigh
Categories: cs.LG
Source:arXiv (Atom API + RSS)T1observed 3 h agohigh
PDF: arxiv.org/pdf/2609.11538
Source:arXiv (Atom API + RSS)T1observed 3 h agohigh
Primary category: cs.LG
Source:arXiv (Atom API + RSS)T1observed 3 h agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 3 h agohigh

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9

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T19

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3 h ago

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Authors: Tiago da Silva, Diego Mesquita, Salem Lahlou

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Value	Valid from → to	Status	Source	Confidence	Extractor
11 Sept 2026	11 Sept 2026 21:02 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
New paperPaperParticle GFlowNets: Rethinking Generative Marginalization Models
New paper: Particle GFlowNets: Rethinking Generative Marginalization Models
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Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.LG	feed	T1· Official	1 h ago	1

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