Improving Cross-Problem Vehicle Routing with Locally Augmented Preferences and Representation Disentanglement
Score
75提出了两项新颖且互补的方法，显著超越现有神经多任务求解器。
实验设计全面，包含多变体评估和消融研究，技术细节扎实。
对物流与智能调度场景具有直接应用潜力，且方法模型无关。
多任务VRP是当前组合优化与强化学习交叉领域的活跃研究方向。
来源为arXiv论文预印本，尚未经过同行评审但属于学术渠道。
Base 83.5 × T2 Source weight ×0.9 = 75
Curation line 70Selected
Why selected
The paper presents high novelty, thorough experiments, and clear advancements in cross-problem VRP solving.
AI commentary
「This is a noteworthy contribution to multi-task combinatorial optimization. POLAR and PLE target training signals and representation separation respectively, and their combination yields clear gains. It is worth reading for researchers in logistics algorithms and reinforcement learning.」
Quick overview
Multi-task vehicle routing problem (VRP) solvers aim to handle multiple VRP variants within a single unified model, avoiding training a separate model for each variant. Existing methods face limitations in training and architecture: reinforcement learning suffers from reward-scale disparities and shrinking advantage signals, while preference optimization stagnates when sampled tours become near-identical; fully shared encoders entangle constraint-dependent representations. This paper proposes two model-agnostic contributions: POLAR, a training algorithm that applies a local search refinement pass to the best decoded tour before forming preference pairs, yielding more informative pairwise margins; and PLE, an encoder that routes each layer through one shared expert and task-specific experts via a gating mechanism, progressively separating common routing structure from constraint-specific encodings. Experiments on various VRP variants show POLAR and PLE together elevate the state-of-the-art among neural multi-task solvers, reducing the average gap to reference solutions by 21.3% relative to the strongest baseline on 16 in-distribution variants, and outperforming prior neural methods on 27 out of 32 unseen variants.
- Proposes POLAR training algorithm that uses local search refinement to generate more informative preference pairs, mitigating signal decay in preference optimization
- Proposes PLE encoder architecture that routes through shared and task-specific experts via gating, disentangling common and constraint-specific representations
- Reduces average gap to reference solutions by 21.3% relative to strongest baseline on 16 in-distribution VRP variants, outperforming prior neural methods on 27 of 32 unseen variants
- Ablation studies confirm both contributions improve cross-problem generalization across multiple backbone architectures
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