  Role-playing agents (RPA) have been a popular application area for large
language models (LLMs), attracting significant interest from both industry and
academia.While existing RPAs well portray the characters' knowledge and tones,
they face challenges in capturing their minds, especially for small
role-playing language models (RPLMs). In this paper, we propose to enhance
RPLMs via personality-indicative data. Specifically, we leverage questions from
psychological scales and distill advanced RPAs to generate dialogues that grasp
the minds of characters. Experimental results validate that RPLMs trained with
our dataset exhibit advanced role-playing capabilities for both general and
personality-related evaluations. Code and data are available at
\href{https://github.com/alienet1109/RolePersonality}{this URL}.
