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

2026-02-12

AI Summary

This work addresses the slow convergence of conventional GFlowNets in large-scale state spaces, which stems from undirected exploration. To overcome this limitation, the authors propose a planner-based approach that partitions the state space into overlapping local subregions and introduces a heuristic region-switching mechanism. This mechanism guides the agent toward high-reward regions while avoiding unproductive exploration. The method accelerates convergence and improves sample quality without compromising diversity. Experimental results across multiple benchmark datasets demonstrate that the proposed approach not only speeds up training but also generates samples with higher rewards and greater diversity compared to existing methods.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Multiagent Systems: Multiagent Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

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.

Problem

• GFlowNets
• large state spaces
• convergence
• state space exploration
• reward maximization

Innovation

• GFlowNets
• state space partitioning
• partial exploration
• heuristic switching
• convergence acceleration