DeepSAGE: Stage-Aware Reinforcement Learning for Structured CBT Counseling Dialogue

2026-08-23

AI Summary

为解决LLM咨询代理缺乏结构化目标导向的问题，提出DeepSAGE框架，结合深度强化学习和阶段意识进行CBT对话。

Abstract

Large Language Model (LLM)-based counseling agents can generate fluent and supportive responses, but they often lack the structured, goal-directed progression required to conduct a coherent therapeutic session. We present DeepSAGE (Strategic AI Guidance Engine), a hybrid LLM--Deep Reinforcement Learning (DRL) framework for stage-aware counseling dialogue grounded in the first session of Cognitive Behavioral Therapy (CBT). DeepSAGE represents the session as eleven stages with explicit therapeutic objectives, with an external controller determines stage completion and the DRL model selects therapeutic intentions that guide LLM response generation. We evaluate DeepSAGE against six retrieval-, prompting-, stage-, and policy-based alternatives. DeepSAGE elicits higher simulated client engagement and openness and achieves the strongest balance of stage-goal completion and dialogue efficiency among stage-structured systems. Domain expert review further indicates that the generated conversations exhibit broadly plausible emotional trajectories and recognizable CBT processes. Because the evaluation relies primarily on simulated clients and model-based metrics, these findings demonstrate comparative dialogue-control improvements rather than clinical effectiveness. These results suggest that combining stage-structured dialogue with learned strategy selection is a promising approach for AI counseling, though clinical effectiveness, safety, and real-world utility require further human evaluation.

Problem

• Large Language Model
• Counseling Dialogue
• Structured Progression
• Therapeutic Session

Innovation

• Stage-Aware Reinforcement Learning
• Structured CBT Counseling Dialogue
• Hybrid LLM-DRL Framework
• Therapeutic Objectives