arXiv AI
Learning Shortest Paths with Generative Flow Networks
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arXiv Machine Learning
Jun 5
Your GFlowNet Secretly Learns an Optimal Transport Plan
arXiv:2606. 06272v1 Announce Type: new Abstract: Generative Flow Networks (GFlowNets) are a framework for sampling structured objects via stochastic trajectories in a directed graph.
arXiv Machine Learning
Aug 6
GFlowNet Training by Policy Gradients
arXiv:2408. 05885v3 Announce Type: replace Abstract: Generative Flow Networks (GFlowNets) have been shown effective to generate combinatorial objects with desired properties.
arXiv Machine Learning
Aug 31
Trajectory balance: Improved credit assignment in GFlowNets
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.
arXiv Machine Learning
Sep 11
Partial GFlowNet: Accelerating Convergence in Large State Spaces via Strategic Partitioning
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.
arXiv Machine Learning
Jul 10
MPFlow: Learning Budgeted Max-Flow Optimization on the Lightning Network with Deep Graph Reinforcement Learning
arXiv:2607. 08703v1 Announce Type: new Abstract: We address liquidity placement in the Bitcoin Lightning Network (LN): given a fixed budget, which channels should a node open to maximize its routing capacity?
arXiv AI
Jul 7
Graph Neural Networks are Heuristics
arXiv:2601. 13465v4 Announce Type: replace Abstract: Graph neural networks are usually treated as auxiliaries for combinatorial optimization: they imitate algorithms, guide search, or supply scores to classical procedures.