Existing methods for AI psychological counselors predominantly rely on supervised fine-tuning using static dialogue datasets. However, this contrasts with human experts, who continuously refine their proficiency through clinical practice and accumulated experience. To bridge this gap, we propose an Experience-Driven Lifelong Learning Agent (\texttt{PsychAgent}) for psychological counseling. First, we establish a Memory-Augmented Planning Engine tailored for longitudinal multi-session interactions, which ensures therapeutic continuity through persistent memory and strategic planning. Second, to support self-evolution, we design a Skill Evolution Engine that extracts new practice-grounded skills from historical counseling trajectories. Finally, we introduce a Reinforced Internalization Engine that integrates the evolved skills into the model via rejection fine-tuning, aiming to improve performance across diverse scenarios. Comparative analysis shows that our approach achieves higher scores than strong general LLMs (e.g., GPT-5.4, Gemini-3) and domain-specific baselines across all reported evaluation dimensions. These results suggest that lifelong learning can improve the consistency and overall quality of multi-session counseling responses.

翻译：现有AI心理咨询方法主要依赖基于静态对话数据集的监督微调。然而，这与人专家通过临床实践和积累经验持续提升专业能力的方式形成鲜明对比。为弥合这一差距，我们提出一种用于心理咨询的经验驱动终身学习智能体（\texttt{PsychAgent}）。首先，我们构建了一个专为纵向多轮互动设计的记忆增强规划引擎，通过持久性记忆和战略规划确保治疗连贯性。其次，为支持自我进化，我们设计了一个技能进化引擎，可从历史咨询轨迹中提取基于实践的新技能。最后，我们引入强化内化引擎，通过拒绝微调将进化后的技能整合到模型中，旨在提升跨场景性能。对比分析显示，在所有评估维度上，我们的方法均优于强大的通用大语言模型（如GPT-5.4、Gemini-3）及领域特定基线。结果表明，终身学习能提升多轮咨询响应的连贯性和整体质量。