English(EN) Stable GFlowNets with Probabilistic Guarantees

稳定的GFlowNets算法提高了训练稳定性和保真度

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-03 05:57

研究人员推出了一种名为Stable GFlowNets的算法，旨在解决生成流网络（GFlowNets）的训练不稳定性问题。这些网络用于以与奖励成比例的概率采样状态，但经常遭受损失尖峰和模式崩溃等问题。新方法提供了概率保证和理论界限来稳定训练，从而提高了分布保真度和一致性。 AI

影响为生成模型提供更稳定的训练方法，有可能提高其在复杂采样任务中的可靠性和性能。

排序理由学术论文，详细介绍了一种用于提高GFlowNet训练稳定性的新算法。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv stat.ML TIER_1 English(EN) · Zengxiang Lei, Ananth Shreekumar, Jonathan Rosenthal, Ruoyu Song, Alvaro A. Cardenas, Daniel J. Fremont, Dongyan Xu, Satish Ukkusuri, Z. Berkay Celik · 2026-05-05 04:00

Stable GFlowNets with Probabilistic Guarantees

arXiv:2605.01729v1 Announce Type: cross Abstract: Generative Flow Networks (GFlowNets) learn to sample states proportional to an unnormalized reward. Despite their theoretical promise, practical training is often unstable, exhibiting severe loss spikes and mode collapse. To tackl…
arXiv stat.ML TIER_1 English(EN) · Z. Berkay Celik · 2026-05-03 05:57

Stable GFlowNets with Probabilistic Guarantees

Generative Flow Networks (GFlowNets) learn to sample states proportional to an unnormalized reward. Despite their theoretical promise, practical training is often unstable, exhibiting severe loss spikes and mode collapse. To tackle this, we first assess the sensitivity of GFlowNe…