千亿参数神经路由模型突破传统路径优化瓶颈
LRM-1B: Towards Large Routing Model
- 构建10亿参数神经路由模型，模仿大模型扩展思路
- 在多种路径问题上达到当前最优，小规模仍具优势
- 揭示模型规模与性能的幂律关系，指导未来设计
车辆路径问题（VRPs）是组合优化的核心，具有重要实际意义。近年来，神经组合优化（NCO）利用神经网络在解决VRP方面取得进展，但该领域对模型规模化的探索仍不充分。受大语言模型成功启发，本文提出一个拥有10亿参数的大型路由模型（LRM-1B），以应对多样化的VRP场景。我们在多个问题变体、分布和规模下对LRM-1B进行了全面评估，取得了当前最优结果。研究发现，LRM-1B不仅可适应不同挑战，还展现出卓越性能，优于现有模型。此外，我们分析了从100万到10亿参数的神经路由模型缩放行为，确认多个模型因子与性能之间存在幂律关系，为构建基础神经路由求解器提供了关键配置启示。
原文摘要 · Abstract (English)
Vehicle routing problems (VRPs) are central to combinatorial optimization with significant practical implications. Recent advancements in neural combinatorial optimization (NCO) have demonstrated promising results by leveraging neural networks to solve VRPs, yet the exploration of model scaling within this domain remains underexplored. Inspired by the success of model scaling in large language models (LLMs), this study introduces a Large Routing Model with 1 billion parameters (LRM-1B), designed to address diverse VRP scenarios. We present a comprehensive evaluation of LRM-1B across multiple problem variants, distributions, and sizes, establishing state-of-the-art results. Our findings reveal that LRM-1B not only adapts to different VRP challenges but also showcases superior performance, outperforming existing models. Additionally, we explore the scaling behavior of neural routing models from 1M to 1B parameters. Our analysis confirms power-law between multiple model factors and performance, offering critical insights into the optimal configurations for foundation neural routing solvers.
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