SOURCE-LINKED INTELLIGENCE

Improving Cross-Problem Vehicle Routing with Locally Augmented Preferences and Representation Disentanglement

arXiv · AI, language, vision and robotics · article · Aug 25, 2026 · UTC

Multi-task vehicle routing problem (VRP) solvers seek to handle multiple VRP variants within a single unified model, avoiding the need to train a separate model for every variant. In spite of recent progress, current approaches remain limited on two fronts. On the training side, reinforcement learning suffers from reward-scale disparities and shrinking advantage signals as policies improve, whereas preference optimization stagnates once sampled tours become near-identical and thus fundamentally limited by the quality of the policy's own generated solutions, leaving both paradigms with weak sup

Read original source ↗ Open in workspace

recordType

paper

region

Global