  Solving NP-hard/complete combinatorial problems with neural networks is a
challenging research area that aims to surpass classical approximate
algorithms. The long-term objective is to outperform hand-designed heuristics
for NP-hard/complete problems by learning to generate superior solutions solely
from training data. Current neural-based methods for solving CO problems often
overlook the inherent "algorithmic" nature of the problems. In contrast,
heuristics designed for CO problems, e.g. TSP, frequently leverage
well-established algorithms, such as those for finding the minimum spanning
tree. In this paper, we propose leveraging recent advancements in neural
algorithmic reasoning to improve the learning of CO problems. Specifically, we
suggest pre-training our neural model on relevant algorithms before training it
on CO instances. Our results demonstrate that by using this learning setup, we
achieve superior performance compared to non-algorithmically informed deep
learning models.
