arXiv Machine Learning September 11, 2026 By Tiago da Silva, Diego Mesquita, Salem Lahlou

Particle GFlowNets: Rethinking Generative Marginalization Models

Read the original on arXiv Machine Learning →

The paper introduces Particle GFlowNets, showing that Generative Marginalization Models (MaMs) are equivalent to Generative Flow Networks. It extends MaMs to non‑autoregressive sampling and proposes an automatic full‑state rejuvenation criterion based on the Gelman‑Rubin statistic to accelerate learning. Experiments demonstrate significant training speedups in large combinatorial spaces.

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