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
arXiv:2509. 00704v2 Announce Type: replace Abstract: The scalability of pool-based active learning is limited by the computational cost of evaluating large unlabeled datasets, a challenge that is particularly acute in virtual screening for drug discovery.
By Renfei Zhang, Mohit Pandey, Artem Cherkasov, Martin Ester
The paper introduces a new learning objective called trajectory balance for Generative Flow Networks (GFlowNets), aiming to improve credit assignment across long action sequences. It demonstrates that minimizing this objective yields a policy that samples exactly from the target distribution. Experiments on four domains show that trajectory balance enhances convergence, sample diversity, and robustness to long sequences and large action spaces.
By Esmeralda S. Whitammer, Moksh Jain, Emmanuel Bengio, Chen Sun, Yoshua Bengio
arXiv:2606. 08802v1 Announce Type: new Abstract: Standard flow and diffusion pre-training matches the distribution of available data (e.
By Riccardo De Santi, Bruce Lee, Cristian Perez Jensen, Kimon Protopapas, Sophia Tang, Cheng-Hao Liu, Pranam Chatterjee, Yisong Yue, Andreas Krause
The paper introduces Wasserstein‑Tilted Flow Maps (WTF), a simulation‑free reinforcement learning method that fine‑tunes pre‑trained flow‑based generative models by adding an optimal transport regularizer derived from the model’s drift. Unlike traditional KL‑reward tilting, WTF transports individual samples toward higher reward, framing the problem as a deterministic optimal control task on the flow map. Experiments on ImageNet‑256 and text‑to‑image demonstrate that WTF achieves higher reward and comparable or better diversity while reducing training compute by up to 280×.
By Abbas Mammadov, Jerry Y. Huang, Justin Lin, Partha Kaushik, Sheel Shah, Kartik Nair, Yee Whye Teh, Nicholas M. Boffi
The paper introduces a method that integrates action abstraction into policy optimization for reinforcement learning and generative flow networks. By iteratively identifying frequently used action subsequences in high‑reward trajectories and treating them as single high‑level actions, the approach expands the action space and improves sample efficiency. Experiments on synthetic and real‑world tasks show that this technique discovers diverse high‑reward states more effectively, especially on challenging exploration problems, and yields interpretable abstract actions that reflect the underlying reward structure.
By Oussama Boussif, L\'ena N\'ehale Ezzine, Joseph D Viviano, Micha{\l} Koziarski, Moksh Jain, Esmeralda S. Whitammer, Emmanuel Bengio, Rim Assouel, Yoshua Bengio
arXiv:2605. 01729v2 Announce Type: replace Abstract: Generative Flow Networks (GFlowNets) learn to sample states proportional to an unnormalized reward.
By Zengxiang Lei, Ananth Shreekumar, Jonathan Rosenthal, Ruoyu Song, Alvaro A. Cardenas, Daniel J. Fremont, Dongyan Xu, Satish Ukkusuri, Z. Berkay Celik