In proactive dialogue, the challenge lies not just in generating responses
but in steering conversations toward predetermined goals, a task where Large
Language Models (LLMs) typically struggle due to their reactive nature.
Traditional approaches to enhance dialogue planning in LLMs, ranging from
elaborate prompt engineering to the integration of policy networks, either face
efficiency issues or deliver suboptimal performance. Inspired by the
dualprocess theory in psychology, which identifies two distinct modes of
thinking - intuitive (fast) and analytical (slow), we propose the Dual-Process
Dialogue Planning (DPDP) framework. DPDP embodies this theory through two
complementary planning systems: an instinctive policy model for familiar
contexts and a deliberative Monte Carlo Tree Search (MCTS) mechanism for
complex, novel scenarios. This dual strategy is further coupled with a novel
two-stage training regimen: offline Reinforcement Learning for robust initial
policy model formation followed by MCTS-enhanced on-the-fly learning, which
ensures a dynamic balance between efficiency and strategic depth. Our empirical
evaluations across diverse dialogue tasks affirm DPDP's superiority in
achieving both high-quality dialogues and operational efficiency, outpacing
existing methods.

通过引入心理学中的双过程理论，我们提出了双过程对话规划（DPDP）框架，结合直觉策略模型和深思熟虑的蒙特卡洛树搜索机制，通过线下强化学习和增强的实时学习实现了效率和策略深度之间的动态平衡，并在各种对话任务中实验证实了 DPDP 在实现高质量对话和操作效率上的卓越性。