@inproceedings{wang-etal-2025-video,
    title = "Video-{RTS}: Rethinking Reinforcement Learning and Test-Time Scaling for Efficient and Enhanced Video Reasoning",
    author = "Wang, Ziyang  and
      Yoon, Jaehong  and
      Yu, Shoubin  and
      Islam, Md Mohaiminul  and
      Bertasius, Gedas  and
      Bansal, Mohit",
    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.1428/",
    pages = "28114--28128",
    ISBN = "979-8-89176-332-6",
    abstract = "Despite advances in reinforcement learning (RL)-based video reasoning with large language models (LLMs), data collection and fine- tuning remain significant challenges. These methods often rely on large-scale supervised fine-tuning (SFT) with extensive video data and long Chain-of-Thought (CoT) annotations, making them costly and hard to scale. To address this, we present Video-RTS, a new approach to improve video reasoning capability with drastically improved data efficiency by combining data-efficient RL with a video-adaptive test-time scaling (TTS) strategy. Building on observations about the data scaling, we skip the resource-intensive SFT step and employ efficient pure-RL training with output-based rewards, requiring no additional annotations or extensive fine-tuning. Furthermore, to utilize computational resources more efficiently, we introduce a sparse-to-dense video TTS strategy that improves inference by iteratively adding frames based on output consistency. We validate our approach on multiple video reasoning benchmarks, showing that Video-RTS surpasses existing video reasoning models by 2.4{\%} in accuracy using only 3.6{\%} training samples. Specifically, Video-RTS achieves a 4.2{\%} improvement on Video-Holmes, a recent and challenging video reasoning benchmark. Notably, our pure RL training and adaptive video TTS offer complementary strengths, enabling Video-RTS{'}s strong reasoning performance."
}