@article{kim2026persona-pruner,
author = {Kim, Jinsu and Tack, Jihoon and Lee, Noah and Jeong, Jongheon},
title = {Persona-Pruner: Sculpting Lightweight Models for Role-Playing},
year = {2026},
month = {June},
abstract = {Language Models (LMs) have shown remarkable potential as role-playing chatbots, delivering consistent, stylized interactions when given a specification of a character or user persona. However, applying these capabilities to real-world applications (e.g., ecosystems with numerous NPCs interacting simultaneously) exposes a critical inefficiency due to the excessive computational cost. In this paper, we question the necessity of dedicating a full, generalist model to a single persona, hypothesizing that a specific character identity relies on only a fraction of the model's total capacity. We observe that naively pruning LMs often severely degrades the role-playing performance for a specific persona; it does not distinguish between redundant knowledge and essential character traits. We propose Persona-Pruner, a framework that sculpts a lightweight role-playing model by isolating persona-specific sub-networks from a single description. Our experiments consistently show that Persona-Pruner preserves role-playing performance substantially more effectively than existing state-of-the-art LLM pruning techniques, reducing the performance drop from the dense model by up to 93.8% over the strongest baseline on RoleBench in LLM-as-a-judge score, while still maintaining general LLM capabilities. Code is available at https://github.comhttps://www.microsoft.com/jsu-kim/Persona-Pruner.},
url = {http://approjects.co.za/?big=en-us/research/publication/persona-pruner-sculpting-lightweight-models-for-role-playing/},
journal = {ArXiv},
volume = {abs/2606.14695},
edition = {arXiv.org},
}