Summary
This arXiv paper (2608.24859) by Arthur Corrêa, Paulo Nascimento, and Samuel Moniz addresses limitations in multi-task vehicle routing problem (VRP) solvers, which aim to handle multiple VRP variants within a single unified model instead of training separate models per variant. The authors identify weaknesses on two fronts. On training, reinforcement learning suffers from reward-scale disparities and shrinking advantage signals as policies improve, while preference optimization stagnates once sampled tours become near-identical, limiting both paradigms to weak supervision. On architecture, fully shared encoders entangle constraint-dependent representations across heterogeneous variants, hurting generalization. The paper introduces two model-agnostic contributions: POLAR, a novel training algorithm that applies local search refinement before forming preference pairs over the best decoded tours, yielding more informative pairwise margins; and PLE, an encoder that uses gating mechanisms to route each encoder layer through one shared expert plus a set of task-specific experts, progressively disentangling generic route structure from constraint-specific encoding. Together these aim to improve cross-problem generalization and training signal quality for unified neural VRP solvers.
Paper Overview
Research area: Machine Learning
Authors: Arthur Corrêa, Paulo Nascimento, Samuel Moniz
Published: 2026-08-25
arXiv: 2608.24859
Abstract (translated summary)
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. Despite recent progress, current approaches remain limited on two fronts.
- Training: Reinforcement learning suffers from reward-scale disparities and shrinking advantage signals as policies improve, whereas preference optimization stagnates once sampled tours become near-identical — fundamentally limited by the quality of the policy's own generated solutions. Both paradigms thus face weak supervision as training progresses.
- Architecture: Existing fully shared encoders entangle constraint-dependent representations across heterogeneous variants, limiting generalization.
The authors close these gaps with two model-agnostic contributions:
1. POLAR — a novel training algorithm that applies local search refinement before forming preference pairs over the best decoded tours, producing more informative pairwise margins.
2. PLE — an encoder that routes each encoder layer through one shared expert and a set of task-specific experts via gating mechanisms, progressively disentangling generic route structure from constraint-specific encoding.
Links
- arXiv: https://arxiv.org/abs/2608.24859
---
*Auto-collected on 2026-08-27*
This page is an English static mirror generated for search and AI citation.
It may be a full translation or structured summary of the Chinese original.
Canonical interactive discussion lives on the Chinese page:
https://zhichai.net/topic/178634088