@inproceedings{wang-etal-2025-flexible,
    title = "Flexible Thinking for Multimodal Emotional Support Conversation via Reinforcement Learning",
    author = "Wang, Fanfan  and
      Shen, Xiangqing  and
      Yu, Jianfei  and
      Xia, Rui",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.70/",
    doi = "10.18653/v1/2025.findings-emnlp.70",
    pages = "1341--1356",
    ISBN = "979-8-89176-335-7",
    abstract = "Emotional Support Conversation (ESC) systems aim to alleviate user distress. However, current Chain-of-Thought based ESC methods often employ rigid, text-only reasoning, limiting adaptability in dynamic, multimodal interactions and introducing reasoning noise that degrades support quality. To address this, we introduce ``Flexible Thinking'' for multimodal ESC, enabling models to adaptively select contextually relevant thinking aspects: Visual Scene, Emotion, Situation, and Response Strategy. We first construct training data by manually curating flexible thinking demonstrations on the MESC dataset, then using a Multimodal Large Language Model to synthesize these processes for the full training set. Then, we propose FIRES, a framework integrating Supervised Fine-Tuning (SFT) for initial learning with Reinforcement Learning for refinement. This two-stage approach helps FIRES transcend SFT{'}s generalization limits and, crucially, directly links thinking processes to response quality via tailored rewards, moving beyond imitating potentially imperfect synthetic data. Experiments on MESC and EMOTyDA datasets demonstrate FIRES{'}s effectiveness and generalizability in fostering higher-quality emotional support responses through adaptive reasoning."
}