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.
By Dinghuai Zhang, Hanjun Dai, Esmeralda S. Whitammer, Aaron Courville, Yoshua Bengio, Ling Pan
arXiv:2606. 02294v1 Announce Type: new Abstract: Operations research practitioners typically tackle NP-hard combinatorial problems using large neighborhood search (LNS), a scalable heuristic that iteratively refines a current solution by locally re-optimizing subsets of its variables.
By Germain Vivier-Ardisson, Laurent Demonet, Axel Parmentier, Mathieu Blondel
arXiv:2310.04363v3 Announce Type: replace
Abstract: Autoregressive large language models (LLMs) compress knowledge from their training data through next-token conditional distributions. This limits t...
By Edward J. Hu, Moksh Jain, Eric Elmoznino, Younesse Kaddar, Guillaume Lajoie, Yoshua Bengio, Esmeralda S. Whitammer
arXiv:2501. 17377v4 Announce Type: replace-cross Abstract: Deep Reinforcement Learning (DRL) has emerged as a promising approach for solving Combinatorial Optimization (CO) problems, such as the 3D Bin Packing Problem (3D-BPP), Traveling Salesman Problem (TSP), or Vehicle Routing Problem (VRP), but these neural solvers often exhibit brittleness when facing distribution shifts.
By Han Fang, Paul Weng, Yutong Ban
arXiv:2606. 07400v1 Announce Type: new Abstract: Many scientific problems require inferring unobserved mechanistic latent states from indirect observations.
By Stefan Ivanovic, Ge Liu, Mohammed El-Kebir
arXiv:2606. 19750v1 Announce Type: cross Abstract: Reinforcement learning (RL) is a central approach for improving reasoning capabilities in large language models (LLMs), where training efficiency depends critically on how problems are sampled during optimization.
By Darrien McKenzie, Nicklas Hansen, Xiaolong Wang