让AI心理咨询更懂方法，用可解释的思考过程提升辅导效果
MIThinker: A Plug-and-Play Policy-Optimized Thinker For Motivational Interviewing Counseling
- 引入可插拔的思维模块，让AI生成与咨询技术对齐的治疗性思考
- 仅需1/10计算量，达成与顶尖系统相当的咨询能力
- 通过反向工程自动构建思考数据，解决无标注数据难题
推理型大语言模型在复杂问题求解中取得进展，通过内部推理（或思考）引导解答生成。然而，现有基于LLM的心理咨询代理，包括采用动机访谈（MI）的方法，生成回复时未显式对齐思考过程与咨询技巧，限制了其效果。我们提出MIThinker，一种轻量级思维模型，能生成指导性治疗思考，用于辅助MI咨询代理进行策略选择与回复生成。为解决缺乏标注思考数据的问题，我们设计了AugR1-MI自动化流程，从观察到的回复中逆向推导咨询师的思考过程。通过两阶段训练（监督微调+强化学习），MIThinker在理论心智评估和策略对齐方面表现优异。综合评估显示，使用MIThinker的MindfulMI代理，在计算量低一个数量级的情况下，达到与当前最优系统相当的动机访谈能力。
原文摘要 · Abstract (English)
Reasoning large language models (LLMs) have recently made much progress in complex problem-solving, leveraging internal reasoning (or thought) to guide their solution generation. However, existing LLM-based counseling agents, including those using Motivational Interviewing (MI), generate responses without explicitly aligning thoughts with counseling techniques, limiting their effectiveness. We propose MIThinker, a lightweight thinking model that generates therapeutic thoughts to guide MI counseling agents in strategy selection and response generation. To overcome the lack of annotated thought data, we introduce AugR1-MI, an automated pipeline that reverse-engineers counselor's thoughts from observed responses. Through two-stage training combining supervised fine-tuning and reinforcement learning, MIThinker demonstrates improved theory-of-mind assessment and strategy alignment. Comprehensive evaluations show that MindfulMI, our agent leveraging MIThinker, achieves MI competency comparable to state-of-the-art systems with an order of magnitude less computation.
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