PsychAgent: An Experience-Driven Lifelong Learning Agent for Self-Evolving Psychological Counselor
Yutao Yang, Junsong Li, Qianjun Pan, Jie Zhou, Kai Chen, Qin Chen, Jingyuan Zhao, Ningning Zhou, Xin Li, Liang He

TL;DR
PsychAgent is a lifelong learning AI for psychological counseling that continuously improves through experience, memory, and skill evolution, outperforming existing models in multi-session therapy scenarios.
Contribution
It introduces a memory-augmented planning engine, a skill evolution engine, and a reinforced internalization engine for self-evolving counseling AI.
Findings
Outperforms GPT-5.4 and Gemini-3 in evaluation metrics.
Achieves higher scores than domain-specific baselines.
Enhances consistency and quality in multi-session responses.
Abstract
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…
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