Machine learning systems perform well on pattern matching tasks, but their
ability to perform algorithmic or logical reasoning is not well understood. One
important reasoning capability is algorithmic extrapolation, in which models
trained only on small/simple reasoning problems can synthesize complex
strategies for large/complex problems at test time. Algorithmic extrapolation
can be achieved through recurrent systems, which can be iterated many times to
solve difficult reasoning problems. We observe that this approach fails to
scale to highly complex problems because behavior degenerates when many
iterations are applied -- an issue we refer to as "overthinking." We propose a
recall architecture that keeps an explicit copy of the problem instance in
memory so that it cannot be forgotten. We also employ a progressive training
routine that prevents the model from learning behaviors that are specific to
iteration number and instead pushes it to learn behaviors that can be repeated
indefinitely. These innovations prevent the overthinking problem, and enable
recurrent systems to solve extremely hard extrapolation tasks.

本研究提出了一种记忆回溯网络算法，采用保留问题实例的显式副本以及渐进式训练方法，解决了循环系统复杂问题迭代次数过多导致退化行为的问题，从而使循环系统能够解决极难的算法推理问题。

使用循环神经网络进行端到端算法合成：逻辑推断而不过度思考

End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking

Many natural systems, such as neurons firing in the brain or basketball teams
traversing a court, give rise to time series data with complex, nonlinear
dynamics. We can gain insight into these systems by decomposing the data into
segments that are each explained by simpler dynamic units. Building on
switching linear dynamical systems (SLDS), we present a new model class that
not only discovers these dynamical units, but also explains how their switching
behavior depends on observations or continuous latent states. These "recurrent"
switching linear dynamical systems provide further insight by discovering the
conditions under which each unit is deployed, something that traditional SLDS
models fail to do. We leverage recent algorithmic advances in approximate
inference to make Bayesian inference in these models easy, fast, and scalable.

通过将数据分解为由简单动态单元解释的片段并研究这些切换的行为，我们展示了一种新的模型类型 —— 递归式切换线性动态系统，以提供更深入的洞察力，并利用近年来的算法进展在这些模型中使用近似推理使贝叶斯推理变得轻松、快速且可扩展。