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

arXiv:2608.24859 · cs.LG · Submitted 2026-08-25 · Read on arXiv

cs.LG

Submitted: 2026-08-25

Updated: 2026-08-25

Code: https://github.com/AJ-Correa/Routing-POLAR

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Generalization — 23× in this paper · explained in 13 episodes
• Embeddings — 21× in this paper · explained in 2 episodes
• Vehicle Routing Problem — 14× in this paper
• Combinatorial Optimization — 12× in this paper · explained in 2 episodes
• Preference Optimization — 12× in this paper
• Ablation — 11× in this paper · explained in 1 episode
• Ablation Studies — 7× in this paper · explained in 1 episode
• Time Windows — 7× in this paper
• Entanglement — 6× in this paper · explained in 3 episodes
• Multi-Task Learning — 6× in this paper
• Reinforcement Learning — 6× in this paper · explained in 4 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Core — 4× in this paper · explained in 2 episodes
• Progressive Layered Extraction — 4× in this paper
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Latent Space — 3× in this paper · explained in 7 episodes
• Markov Decision Process — 3× in this paper
• Mixed Backhauls — 3× in this paper
• Pairwise Comparisons — 3× in this paper · explained in 2 episodes
• Zero-Shot Generalization — 3× in this paper · explained in 5 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• Distribution Shift — 2× in this paper · explained in 5 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Knowledge Distillation — 2× in this paper · explained in 11 episodes
• Model-Agnostic — 2× in this paper · explained in 2 episodes
• Multi-Head Attention — 2× in this paper
• Policy Optimization with Multiple Optima — 2× in this paper
• Preference Optimization with Locally Augmented Refinement — 2× in this paper
• Reward Function — 2× in this paper · explained in 2 episodes
• Traveling Salesman Problem — 2× in this paper
• Ablation study — 1× in this paper · explained in 1 episode
• Action Space — 1× in this paper · explained in 1 episode
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Hybrid Approach — 1× in this paper · explained in 4 episodes
• Markov Decision Process (MDP) — 1× in this paper · explained in 11 episodes
• Modularity — 1× in this paper · explained in 9 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Parameter Efficiency — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Sample Efficiency — 1× in this paper · explained in 4 episodes
• Sparse Attention — 1× in this paper · explained in 5 episodes
• Task Decomposition — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• Zero-Shot Transfer — 1× in this paper · explained in 3 episodes

Sources

• An Overview of Multi-Task Learning in Deep Neural Networks
• GLU Variants Improve Transformer

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