We consider the problem of multi-task reasoning (MTR), where an agent can
solve multiple tasks via (first-order) logic reasoning. This capability is
essential for human-like intelligence due to its strong generalizability and
simplicity for handling multiple tasks. However, a major challenge in
developing effective MTR is the intrinsic conflict between reasoning capability
and efficiency. An MTR-capable agent must master a large set of "skills" to
tackle diverse tasks, but executing a particular task at the inference stage
requires only a small subset of immediately relevant skills. How can we
maintain broad reasoning capability and also efficient specific-task
performance? To address this problem, we propose a Planner-Reasoner framework
capable of state-of-the-art MTR capability and high efficiency. The Reasoner
models shareable (first-order) logic deduction rules, from which the Planner
selects a subset to compose into efficient reasoning paths. The entire model is
trained in an end-to-end manner using deep reinforcement learning, and
experimental studies over a variety of domains validate its effectiveness.

本研究提出了一个计划推理框架并采用深度强化学习的方式进行训练，旨在解决多任务推理中公共的难题，即如何保持广泛的推理能力和高效的特定任务表现。通过共享推理规则和选择合适的推理路径，该模型在多个领域的实验中取得了良好的效果。