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arXiv AI By Pedro Dall'Antonia, Daniel Csillag, Diego Mesquita

Rethinking GFlowNet Training: Negative Reinforce Dynamics

Read the original on arXiv AI →

The paper introduces Negative Reinforce Dynamics (NRD), a new training approach for Generative Flow Networks (GFlowNets) that addresses the exploration–correctness trade‑off inherent in standard semi‑gradient updates. By linking the full gradient to a min‑min dynamic between a GFlowNet and a decoupled sampler, NRD effectively reduces balance errors in high‑error regions while preserving a single‑policy framework. Experiments on standard benchmarks show that NRD speeds up convergence and improves exploration efficiency compared to existing methods, without requiring a separate teacher or auxiliary policy.

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 AI.

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