Complex reasoning aims to draw a correct inference based on complex rules. As a hallmark of human intelligence, it involves a degree of explicit reading comprehension, interpretation of logical knowledge and complex rule application. In this paper, we take a step forward in complex reasoning by systematically studying the three challenging and domain-general tasks of the Law School Admission Test (LSAT), including analytical reasoning, logical reasoning and reading comprehension. We propose a hybrid reasoning system to integrate these three tasks and achieve impressive overall performance on the LSAT tests. The experimental results demonstrate that our system endows itself a certain complex reasoning ability, especially the fundamental reading comprehension and challenging logical reasoning capacities. Further analysis also shows the effectiveness of combining the pre-trained models with the task-specific reasoning module, and integrating symbolic knowledge into discrete interpretable reasoning steps in complex reasoning. We further shed a light on the potential future directions, like unsupervised symbolic knowledge extraction, model interpretability, few-shot learning and comprehensive benchmark for complex reasoning.

在本文中，我们通过系统地研究律师入学考试的三项具有挑战性并且领域通用的任务，包括分析推理、逻辑推理和阅读理解，进一步推进了复杂推理。我们提出了一个混合推理系统，将这三个任务进行整合，并在律师入学考试中获得了令人印象深刻的整体表现。实验结果表明，我们的系统具有一定的复杂推理能力，特别是基本阅读理解和具有挑战性的逻辑推理能力。进一步分析还显示了将预训练模型与任务专用的推理模块相结合，以及将符号知识整合到复杂推理的离散可解释推理步骤中的有效性。我们进一步展示了未来潜在的方向，如无监督符号知识提取、模型可解释性、小样本学习和复杂推理的全面基准。

LSAT：复杂推理的进展和挑战