English(EN) Rethinking GFlowNet Training: Negative Reinforce Dynamics

新的 GFlowNet 训练方法提高了探索和收敛性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-31 04:00

研究人员推出了一种新颖的生成流网络（GFlowNets）训练方法——负强化动力学（NRD）。与依赖半梯度并将采样分布视为固定的现有方法不同，NRD 利用全梯度揭示了一种反探索机制。该方法将双人博弈动力学与单策略动力学联系起来，使采样器能够避开高误差区域并提高探索效率。与当前方法相比，NRD 在标准基准测试中表现出更优越的性能，在无需单独的教师或辅助策略的情况下实现了更快的收敛和更好的探索。 AI

影响引入了一种新颖的 GFlowNets 训练动力学，有望提高其在 AI 驱动的科学发现和采样任务中的效率。

排序理由详细介绍 GFlowNets 新训练方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Pedro Dall'Antonia, Daniel Csillag, Diego Mesquita · 2026-10-31 04:00

重新思考 GFlowNet 训练：负强化动力学

arXiv:2610.38994v1 Announce Type: cross Abstract: Generative Flow Networks (GFlowNets) are models that learn to sample discrete objects proportional to a target reward. They are trained by minimizing balance errors over sampled trajectories, which inherently ties correctness to e…

本文如何被排名

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重新思考 GFlowNet 训练：负强化动力学

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