通过系统实验发现，深度扩展比宽度扩展更有效提升路径规划模型性能。
Efficient Decoder Scaling Strategy for Neural Routing Solvers
- 对比深度与宽度两种扩展方式，系统评估其在12个模型上的表现。
- 深度扩展带来显著性能提升，参数效率优于宽度扩展。
- 适合关注神经路由求解器高效设计的研究者与工程师。
基于构造的神经路由求解器通常由编码器和解码器组成，是解决车辆路径问题的有前景方法。尽管已有研究指出将参数从编码器转向解码器可提升性能，但多数工作将解码器规模限制在1-3M参数，对大规模扩展的影响尚不明确。为此，我们系统比较了深度扩展与宽度扩展两种策略，构建了12种模型配置，参数量范围为1M至约150M，全面评估其在参数效率、数据效率和计算效率三个维度的表现。实验结果表明，参数总量无法准确预测模型性能，模型深度（层数）与宽度（嵌入维度）起着关键且不同的作用。尤为重要的是，深度扩展相比宽度扩展能带来更优的性能增益。基于此，我们提出并实证验证了一套参数与计算资源高效分配的设计原则，以优化模型性能。
原文摘要 · Abstract (English)
Construction-based neural routing solvers, typically composed of an encoder and a decoder, have emerged as a promising approach for solving vehicle routing problems. While recent studies suggest that shifting parameters from the encoder to the decoder enhances performance, most works restrict the decoder size to 1-3M parameters, leaving the effects of scaling largely unexplored. To address this gap, we conduct a systematic study comparing two distinct strategies: scaling depth versus scaling width. We synthesize these strategies to construct a suite of 12 model configurations, spanning a parameter range from 1M to ~150M, and extensively evaluate their scaling behaviors across three critical dimensions: parameter efficiency, data efficiency, and compute efficiency. Our empirical results reveal that parameter count is insufficient to accurately predict the model performance, highlighting the critical and distinct roles of model depth (layer count) and width (embedding dimension). Crucially, we demonstrate that scaling depth yields superior performance gains to scaling width. Based on these findings, we provide and experimentally validate a set of design principles for the efficient allocation of parameters and compute resources to enhance the model performance.
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