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Partial GFlowNet: Accelerating Convergence in Large State Spaces via Strategic Partitioning

arXiv 2602.11498 ↗First posted 12 Feb 2026cs.LGcited by 0

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Abstract (arXiv)

Generative Flow Networks (GFlowNets) have shown promising potential to generate high-scoring candidates with probability proportional to their rewards. As existing GFlowNets freely explore in state space, they encounter significant convergence challenges when scaling to large state spaces. Addressing this issue, this paper proposes to restrict the exploration of actor. A planner is introduced to partition the entire state space into overlapping partial state spaces. Given their limited size, these partial state spaces allow the actor to efficiently identify subregions with higher rewards. A heuristic strategy is introduced to switch partial regions thus preventing the actor from wasting time exploring fully explored or low-reward partial regions. By iteratively exploring these partial state spaces, the actor learns to converge towards the high-reward subregions within the entire state space. Experiments on several widely used datasets demonstrate that modelname converges faster than existing works on large state spaces. Furthermore, modelname not only generates candidates with higher rewards but also significantly improves their diversity.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 27 references that are in the map. First posted on arXiv 12 Feb 2026.

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22 of 27 references.

References · 22

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2018Junction Tree Variational Autoencoder for Molecular Graph Generationcited by 647
  3. 2021Flow Network based Generative Models for Non-Iterative Diverse Candidate Generationcited by 384
  4. 2021GFlowNet Foundationscited by 286
  5. 2022Trajectory balance: Improved credit assignment in GFlowNetscited by 244
  6. 2022Biological Sequence Design with GFlowNetscited by 172
  7. 2022Bayesian Structure Learning with Generative Flow Networkscited by 153
  8. 2021MARS: Markov Molecular Sampling for Multi-objective Drug Discoverycited by 125
  9. 2022Generative Flow Networks for Discrete Probabilistic Modelingcited by 113
  10. 2023A theory of continuous generative flow networkscited by 98
  11. 2023Better Training of GFlowNets with Local Credit and Incomplete Trajectoriescited by 83
  12. 2020AdaLead: A simple and robust adaptive greedy search algorithm for sequence designcited by 63
10 more
  1. 2023Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimizationcited by 57
  2. 2022Generative Augmented Flow Networkscited by 48
  3. 2023Local Search GFlowNetscited by 45
  4. 2022A Variational Perspective on Generative Flow Networkscited by 39
  5. 2023Stochastic Generative Flow Networkscited by 32
  6. 2023Distributional GFlowNets with Quantile Flowscited by 28
  7. 2023Distributionally Robust Model-based Reinforcement Learning with Large State Spacescited by 17
  8. 2023Pre-Training and Fine-Tuning Generative Flow Networkscited by 16
  9. 2020Exact Reduction of Huge Action Spaces in General Reinforcement Learningcited by 10
  10. 2024Baking Symmetry into GFlowNetscited by 7

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