Recent multi-modal large language models (MLLMs) often struggle to generate personalized image captions, even when trained on high-quality captions. In this work, we observe that such limitations persist in existing post-training-based MLLM personalization methods. Specifically, despite being post-tuned with large-scale caption data through supervised fine-tuning (SFT), these models frequently fail to produce faithful descriptions in real-world scenarios, such as multi-concept image captioning. However, acquiring large-scale, high-quality captions for such complex settings is both costly and difficult. To address the data-centric nature of SFT, we propose a reinforcement learning (RL)-based post-training framework. To the best of our knowledge, this is the first RL-based approach to post-train MLLMs for personalized image captioning. Our method significantly enhances both visual recognition and personalized generation capabilities of MLLMs, and consistently outperforms existing SFT-based baselines, especially in the challenging multi-concept image captioning task. Project page: https://github.com/oyt9306/RePIC
@article{arxiv.2506.18369,
title = {RePIC: Reinforced Post-Training for Personalizing Multi-Modal Language Models},
author = {Yeongtak Oh and Dohyun Chung and Juhyeon Shin and Sangha Park and Johan Barthelemy and Jisoo Mok and Sungroh Yoon},
journal= {arXiv preprint arXiv:2506.18369},
year = {2025}
}