Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets

The paper introduces a method for tackling combinatorial optimization (CO) problems—often NP‑hard—by leveraging GFlowNets to sample solutions from the solution space. It designs Markov decision processes tailored to various CO tasks and trains conditional GFlowNets, incorporating efficient training techniques for long‑range credit assignment. Experiments on synthetic and realistic datasets show that these GFlowNet policies can efficiently locate high‑quality solutions, and the implementation is publicly available.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

• reinforcement-learning