提出稳定训练GFlowNets的新方法，提升生成多样性与可靠性。
Stable GFlowNets with TV Monitoring and Probabilistic Guarantees
- 基于总变差距离设计自适应参考流，缓解训练不稳问题。
- 理论证明损失有界可保证分布精度，实现全局保真度认证。
- 适合需高多样性生成的分子设计与生物序列优化任务。
生成流网络（GFlowNets）能按奖励比例采样多样化结构化对象，已用于分子发现和生物序列设计，其中多高质量候选优于单一最优解。尽管理论前景广阔，实际训练常出现严重损失波动和模式崩溃。本文首先分析GFlowNet目标的敏感性，表明学习分布与目标分布间总变差（TV）距离小，并不意味着训练损失有界。针对此矛盾，我们推导出损失到TV的反向边界，证明有界轨迹平衡损失可保证全局分布保真。最后提出稳定GFlowNets，利用该理论通过自适应参考流稳定训练，改善模式覆盖、鲁棒性与可认证性之间的权衡。
原文摘要 · Abstract (English)
Generative Flow Networks (GFlowNets) sample diverse structured objects in proportion to reward and have been applied to molecular discovery and biological-sequence design, where finding multiple high-quality candidates is more useful than returning a single optimum. Despite their theoretical promise, practical training is often unstable, exhibiting severe loss spikes and mode collapse. To address this, we first assess the sensitivity of GFlowNet objectives, demonstrating that a small Total Variation (TV) distance between the learned and target distributions does not preclude an unbounded training loss. Motivated by this mismatch, we establish converse guarantees by deriving loss-to-TV bounds that certify global fidelity from bounded trajectory balance losses. Lastly, we propose Stable GFlowNets, which leverages our theory to stabilize training via adaptive reference flow and improves the trade-off among mode coverage, robustness, and certifiability.
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