Computational Reducibility Enhances Transferable Graph CO Models

A new arXiv preprint (2603.02462) introduces a graph neural network (GNN) encoder that leverages computational reducibility to improve transfer learning across combinatorial optimization (CO) tasks. The research, announced as a replace-cross update, addresses the challenge of generalizing neural solvers to unseen tasks. The proposed encoder uses a GCON module for expressive message passing and energy-based unsupervised loss functions, achieving competitive performance on individual CO tasks. The authors propose pretraining and fine-tuning strategies informed by computational reducibility, demonstrating effective transfer between Maximum Vertex Cover (MVC), Maximum Independent Set (MIS), and Maximum Clique (MaxClique), as well as in a multi-task setting incorporating MaxCut, Minimum Dominating Set (MDS), and graph coloring. In a leave-one-out multi-task learning setup, pretraining on all but one task almost always accelerates convergence on the held-out task. The paper is available on arXiv under the URL https://arxiv.org/abs/2603.02462.