突破传统限制，让生成模型在有环图中也能稳定训练。
Revisiting Non-Acyclic GFlowNets in Discrete Environments
- 提出非环图上的新理论框架，放宽对图结构的限制。
- 证明固定反向策略下训练仍可保持损失稳定。
- 适合研究生成模型理论或复杂图结构建模的学者。
生成流网络（GFlowNets）是一类学习从给定概率分布中采样对象的生成模型，该分布可能仅知至归一化常数。传统方法依赖于在构造的有向无环图环境中采样轨迹，高度依赖图的无环性。本文重新审视非环图上的理论，提出适用于离散环境的更简洁理论框架。同时，揭示了固定反向策略训练、流函数本质以及熵正则强化学习与非环GFlowNets之间的深层联系，自然推广了原有环状设定下的概念与结论。此外，通过实验重新检验了非环GFlowNet训练中的损失稳定性，并验证了理论发现。
原文摘要 · Abstract (English)
Generative Flow Networks (GFlowNets) are a family of generative models that learn to sample objects from a given probability distribution, potentially known up to a normalizing constant. Instead of working in the object space, GFlowNets proceed by sampling trajectories in an appropriately constructed directed acyclic graph environment, greatly relying on the acyclicity of the graph. In our paper, we revisit the theory that relaxes the acyclicity assumption and present a simpler theoretical framework for non-acyclic GFlowNets in discrete environments. Moreover, we provide various novel theoretical insights related to training with fixed backward policies, the nature of flow functions, and connections between entropy-regularized RL and non-acyclic GFlowNets, which naturally generalize the respective concepts and theoretical results from the acyclic setting. In addition, we experimentally re-examine the concept of loss stability in non-acyclic GFlowNet training, as well as validate our own theoretical findings.
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