@inproceedings{chen-etal-2025-datasets,
    title = "Datasets and Recipes for Video Temporal Grounding via Reinforcement Learning",
    author = "Chen, Ruizhe  and
      Luo, Tianze  and
      Fan, Zhiting  and
      Zou, Heqing  and
      Feng, Zhaopeng  and
      Xie, Guiyang  and
      Zhang, Hansheng  and
      Wang, Zhuochen  and
      Liu, Zuozhu  and
      Huaijian, Zhang",
    editor = "Potdar, Saloni  and
      Rojas-Barahona, Lina  and
      Montella, Sebastien",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2025",
    address = "Suzhou (China)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-industry.66/",
    pages = "983--992",
    ISBN = "979-8-89176-333-3",
    abstract = "Video Temporal Grounding (VTG) aims to localize relevant temporal segments in videos given natural language queries. Despite recent progress with large vision-language models (LVLMs) and instruction-tuning, existing approaches often suffer from limited temporal awareness and poor generalization. In this work, we introduce a two-stage training framework that integrates supervised fine-tuning with reinforcement learning (RL) to improve both the accuracy and robustness of VTG models. Our approach first leverages high-quality curated cold-start data for SFT initialization, followed by difficulty-controlled RL to further enhance temporal localization and reasoning abilities. Comprehensive experiments on multiple VTG benchmarks demonstrate that our method consistently outperforms existing models, particularly in challenging and open-domain scenarios. We conduct an in-depth analysis of training strategies and dataset curation, highlighting the importance of both high-quality cold-start data and difficulty-controlled RL. To facilitate further research and industrial adoption, we release all intermediate datasets, models, and code to the community."
}