  Neural combinatorial optimization (NCO) aims at designing problem-independent
and efficient neural network-based strategies for solving combinatorial
problems. The field recently experienced growth by successfully adapting
architectures originally designed for machine translation. Even though the
results are promising, a large gap still exists between NCO models and classic
deterministic solvers, both in terms of accuracy and efficiency. One of the
drawbacks of current approaches is the inefficiency of training on multiple
problem sizes. Curriculum learning strategies have been shown helpful in
increasing performance in the multi-task setting. In this work, we focus on
designing a curriculum learning-based training procedure that can help existing
architectures achieve competitive performance on a large range of problem sizes
simultaneously. We provide a systematic investigation of several training
procedures and use the insights gained to motivate application of a
psychologically-inspired approach to improve upon the classic curriculum
method.
