The paper introduces Partial GFlowNet, a method that partitions a large state space into overlapping partial state spaces to accelerate convergence of Generative Flow Networks. By restricting the actor’s exploration to these smaller regions and using a heuristic to switch between them, the approach enables efficient identification of high‑reward subregions. Experiments on popular datasets show that Partial GFlowNet converges faster, produces higher‑reward candidates, and improves diversity compared to existing methods.
By Xuan Yu, Xu Wang, Rui Zhu, Yudong Zhang, Yang Wang
arXiv:2210.00580v4 Announce Type: replace
Abstract: This paper builds bridges between two families of probabilistic algorithms: (hierarchical) variational inference (VI), which is typically used to m...
By Esmeralda S. Whitammer, Salem Lahlou, Tristan Deleu, Xu Ji, Edward Hu, Katie Everett, Dinghuai Zhang, Yoshua Bengio
arXiv:2607. 13394v1 Announce Type: cross Abstract: Generative Flow Networks (GFlowNets) offer a promising alternative to reward-maximizing reinforcement learning (RL) for large reasoning models, encouraging diverse reasoning paths by matching reward distributions rather than collapsing to dominant modes.
By Xiaodong Liu, Michael Xu, Jack W. Stokes, Paul Smolensky, Doug Burger, Jianfeng Gao
arXiv:2610.07562v1 Announce Type: cross
Abstract: Learning an ensemble of GFlowNets to sample from a discrete target distribution has become a common approach for achieving better state space explora...
By Tiago da Silva, Amauri H. Souza, Salem Lahlou
arXiv:2602. 21565v3 Announce Type: replace Abstract: Generative Flow Networks (GFlowNets) learn to sample diverse candidates in proportion to a reward function, making them well-suited for scientific discovery, where exploring multiple promising solutions is crucial.
By Seokwon Yoon, Youngbin Choi, Seunghyuk Cho, Seungbeom Lee, MoonJeong Park, Dongwoo Kim
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.
By Pedro Dall'Antonia, Daniel Csillag, Diego Mesquita