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

Abstract

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.

Authors 5

Xuan Yu, Xu Wang, Rui Zhu, Yudong Zhang, Yang Wang

Specification

Official page: arxiv.org/abs/2602.11498
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
Arxiv announce type: replace
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
arXiv id: 2602.11498
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
Categories: cs.LG
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
PDF: arxiv.org/pdf/2602.11498
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
Primary category: cs.LG
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh

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9

Source tiers

T19

Freshest observation

5 h ago

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None

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Authors: Xuan Yu, Xu Wang, Rui Zhu

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Claim history for Official page
Value	Valid from → to	Status	Source	Confidence	Extractor
https://arxiv.org/abs/2602.11498	11 Sept 2026 21:02 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
New paperPaperPartial GFlowNet: Accelerating Convergence in Large State Spaces via Strategic Partitioning
New paper: Partial GFlowNet: Accelerating Convergence in Large State Spaces via Strategic Partitioning
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Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.LG	feed	T1· Official	3 h ago	1

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