@inproceedings{liang-etal-2025-structured,
    title = "Structured Preference Optimization for Vision-Language Long-Horizon Task Planning",
    author = "Liang, Xiwen  and
      Lin, Min  and
      Ruan, Weiqi  and
      Xu, Rongtao  and
      Liu, Yuecheng  and
      Chen, Jiaqi  and
      Lin, Bingqian  and
      Zhuang, Yuzheng  and
      Liang, Xiaodan",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.884/",
    pages = "17501--17526",
    ISBN = "979-8-89176-332-6",
    abstract = "Existing vision-language planning methods perform well on short-horizon tasks but struggle with long-horizon reasoning in dynamic environments due to the difficulty of training models to generate high-quality reasoning processes. To address this, we propose Structured Preference Optimization (SPO), a framework that enhances reasoning and action selection for long-horizon task planning through structured evaluation and optimized training. SPO introduces: 1) Structured Preference Evaluation and Optimization, which evaluates reasoning chains across task relevance, historical consistency (as part of textual coherence), and image awareness (alignment with visual observations) to construct high-quality preference pairs; and 2) Curriculum-Guided Progressive Learning, enabling the model to adapt from simple to complex tasks, thereby improving generalization and robustness. To advance research in vision-language long-horizon task planning, we introduce ExtendaBench, a comprehensive benchmark covering 1,509 tasks across VirtualHome and Habitat 2.0, categorized into ultra-short, short, medium, and long tasks. Experimental results demonstrate that SPO significantly improves reasoning quality and final decision accuracy, outperforming prior methods on long-horizon tasks and underscoring the effectiveness of preference-driven optimization in vision-language task planning. Specifically, SPO achieves a +5.98{\%} GCR and +4.68{\%} SR improvement in VirtualHome and a +3.30{\%} GCR and +2.11{\%} SR improvement in Habitat over the best-performing baselines."
}