We propose MADS (Multi-Agent Dialogue Simulation), a scalable framework for generating persuasive multi-turn dialogues via agent self-play. MADS employs three coordinated agents: User Agents simulating diverse persona-driven behaviors, a Dialog Agent executing task-oriented persuasion strategies and an Optimization Agent evaluating and refining dialogue outcomes. We further validate its effectiveness through users' Chain-of-Attitude (CoA) modeling and dedicated LLMs' persuasion assessment. This approach enables low-cost generation of training data without human annotation, addressing key industry challenges such as lack of user data, cold-start evaluation difficulties, and prompt inefficiency. Applied to a real-world marketing scenario, MADS significantly improved the persuasion capacity of small LLMs, increasing the organic traffic conversion rate by 22.4\% (from 1.83\% to 2.24\%) , demonstrating clear business value.

翻译：我们提出了MADS（多智能体对话模拟），一个通过智能体自我博弈生成具有说服力的多轮对话的可扩展框架。MADS采用三个协同工作的智能体：模拟多样化角色驱动行为的用户智能体、执行任务导向说服策略的对话智能体，以及评估并优化对话结果的优化智能体。我们进一步通过用户的"态度链"建模和专用大语言模型的说服力评估来验证其有效性。该方法能够无需人工标注即可低成本生成训练数据，解决了用户数据缺乏、冷启动评估困难以及提示效率低下等关键行业挑战。在一个真实世界的营销场景中应用MADS，显著提升了小型大语言模型的说服能力，将自然流量转化率提高了22.4%（从1.83%提升至2.24%），展现了明确的商业价值。