  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.
