Neural Algorithmic Reasoning is an emerging area of machine learning focusing on building models which can imitate the execution of classic algorithms, such as sorting, shortest paths, etc. One of the main challenges is to learn algorithms that are able to generalize to out-of-distribution data, in particular with significantly larger input sizes. Recent work on this problem has demonstrated the advantages of learning algorithms step-by-step, giving models access to all intermediate steps of the original algorithm. In this work, we instead focus on learning neural algorithmic reasoning only from the input-output pairs without appealing to the intermediate supervision. We propose simple but effective architectural improvements and also build a self-supervised objective that can regularise intermediate computations of the model without access to the algorithm trajectory. We demonstrate that our approach is competitive to its trajectory-supervised counterpart on tasks from the CLRS Algorithmic Reasoning Benchmark and achieves new state-of-the-art results for several problems, including sorting, where we obtain significant improvements. Thus, learning without intermediate supervision is a promising direction for further research on neural reasoners.

翻译：神经算法推理是机器学习的一个新兴领域，专注于构建能够模仿经典算法（如排序、最短路径等）执行过程的模型。其主要挑战之一在于学习能够泛化到分布外数据（尤其是输入规模显著更大的情况）的算法。关于该问题的近期研究表明，逐步学习算法具有优势，即让模型访问原始算法的所有中间步骤。而在本工作中，我们则聚焦于仅从输入-输出对中学习神经算法推理，无需借助中间监督。我们提出了简单但有效的架构改进，并构建了一种自监督目标函数，可在不访问算法轨迹的情况下对模型的中间计算过程进行正则化。实验表明，在CLRS算法推理基准测试的若干任务上，我们的方法性能可与采用轨迹监督的同类方法相媲美，并在包括排序在内的多个问题中取得了新的最优结果（其中排序任务的提升尤为显著）。因此，无需中间监督的学习范式为神经推理器的进一步研究提供了有前景的方向。