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arXiv Machine Learning By Timofei Gritsaev, Evgeny Katasonov, Ildus Sadrtdinov, Esmeralda S. Whitammer, Dmitry Vetrov

On the Generation Order in GFlowNets

Read the original on arXiv Machine Learning →

The paper investigates how the order in which components are generated in GFlowNets affects learning and convergence. It shows that existing GFlowNet algorithms often fail to discover useful generation orders, and introduces a new training recipe—Learning-Order GFlowNet—that learns interpretable orders without explicit annotations. Experiments demonstrate that the new method improves convergence, and analysis of gradient signal-to-noise ratio suggests that higher SNR correlates with faster convergence and better generation orders.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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