@inproceedings{wang-etal-2025-inference,
    title = "Inference Compute-Optimal Video Vision Language Models",
    author = "Wang, Peiqi  and
      Peng, ShengYun  and
      Zhang, Xuewen  and
      Yu, Hanchao  and
      Yang, Yibo  and
      Huang, Lifu  and
      Liu, Fujun  and
      Wang, Qifan",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.117/",
    pages = "2345--2374",
    ISBN = "979-8-89176-251-0",
    abstract = "This work investigates the optimal allocation of inference compute across three key scaling factors in video vision language models: language model size, frame count, and the number of visual tokens per frame. While prior works typically focuses on optimizing model efficiency or improving performance without considering resource constraints, we instead identify optimal model configuration under fixed inference compute budgets. We conduct large-scale training sweeps and careful parametric modeling of task performance to identify the inference compute-optimal frontier. Our experiments reveal how task performance depends on scaling factors and finetuning data size, as well as how changes in data size shift the compute-optimal frontier. These findings translate to practical tips for selecting these scaling factors."
}