While machine learning methods excel at pattern recognition, they struggle
with complex reasoning tasks in a scalable, algorithmic manner. Recent Deep
Thinking methods show promise in learning algorithms that extrapolate: learning
in smaller environments and executing the learned algorithm in larger
environments. However, these works are limited to symmetrical tasks, where the
input and output dimensionalities are the same. To address this gap, we propose
NeuralThink, a new recurrent architecture that can consistently extrapolate to
both symmetrical and asymmetrical tasks, where the dimensionality of the input
and output are different. We contribute with a novel benchmark of asymmetrical
tasks for extrapolation. We show that NeuralThink consistently outperforms the
prior state-of-the-art Deep Thinking architectures, in regards to stable
extrapolation to large observations from smaller training sizes.

机器学习方法在模式识别方面表现出色，然而在可伸缩的算法推理任务方面仍存在困难。近期的深度思考方法在学习算法的外推方面表现出希望，即在较小的环境中学习并在较大的环境中执行学习到的算法。然而，这些方法局限于对称任务，其中输入和输出维度相同。为了弥补这一差距，我们提出了一种名为 NeuralThink 的新循环架构，能够一致地对称和不对称任务进行外推，其中输入和输出的维度不同。我们通过引入一种新颖的不对称任务基准来做出贡献。我们展示了 NeuralThink 在对称和不对称任务的稳定外推方面始终优于先前最先进的深度思考架构，尤其是对于从较小的训练样本中稳定地外推到较大观测样本。