This paper presents ReasonFormer, a unified reasoning framework for mirroring
the modular and compositional reasoning process of humans in complex
decision-making. Inspired by dual-process theory in cognitive science, the
representation module (automatic thinking) and reasoning modules (controlled
thinking) are decoupled to capture different levels of cognition. Upon the top
of the representation module, the pre-trained reasoning modules are modular and
professional in specific and fundamental reasoning skills (e.g., logic, simple
QA, etc). To mimic the controlled compositional thinking process, different
reasoning modules are dynamically activated and composed in both parallel and
cascaded manners to control what reasoning skills are activated and how deep
the reasoning process will be reached to solve the current problems. The
unified reasoning framework solves multiple tasks with a single model, and is
trained and inferred in an end-to-end manner. Evaluated on 11 datasets
requiring different reasoning skills and complexity, ReasonFormer demonstrates
substantial performance boosts, revealing the compositional reasoning ability.
Few-shot experiments exhibit better generalization ability by learning to
compose pre-trained skills for new tasks with limited data, and decoupling the
representation module and the reasoning modules. Further analysis shows the
modularity of reasoning modules as different tasks activate distinct reasoning
skills at different reasoning depths.

ReasonFormer 是一个统一的推理框架，受认知科学中的双过程理论的启发，它可以模拟人类在复杂决策制定中的模块化和组合推理过程。它由表示模块和推理模块组成，可以捕捉不同层次的认知过程，并模拟组合推理过程来控制推理技能的激活以及推理深度。ReasonFormer 通过单一模型解决多个任务，并在端到端的方式下进行训练和推断。在 11 个需要不同推理技能和复杂度的数据集上进行评估，ReasonFormer 展示了显著的性能提升，揭示了组合推理的能力。而且它在少样本的实验中表现出更好的泛化能力，并解耦表示模块和推理模块。最后通过进一步的分析也揭示了推理模块的模块化特性。