https://arxiv.org/abs/2007.03629 We study the problem of learning efficient algorithms that strongly generalize in the
framework of neural program induction. As highlights, our learned model can perform
sorting perfectly on any input data size we tested on, with O(n log n) complexity,
whilst outperforming hand-coded algorithms, including quick sort, in number of
operations even for list sizes far beyond those seen during training.