  Neural construction models have shown promising performance for Vehicle
Routing Problems (VRPs) by adopting either the Autoregressive (AR) or
Non-Autoregressive (NAR) learning approach. While AR models produce
high-quality solutions, they generally have a high inference latency due to
their sequential generation nature. Conversely, NAR models generate solutions
in parallel with a low inference latency but generally exhibit inferior
performance. In this paper, we propose a generic Guided Non-Autoregressive
Knowledge Distillation (GNARKD) method to obtain high-performance NAR models
having a low inference latency. GNARKD removes the constraint of sequential
generation in AR models while preserving the learned pivotal components in the
network architecture to obtain the corresponding NAR models through knowledge
distillation. We evaluate GNARKD by applying it to three widely adopted AR
models to obtain NAR VRP solvers for both synthesized and real-world instances.
The experimental results demonstrate that GNARKD significantly reduces the
inference time (4-5 times faster) with acceptable performance drop (2-3\%). To
the best of our knowledge, this study is first-of-its-kind to obtain NAR VRP
solvers from AR ones through knowledge distillation.
