Streaming Video Instruction Tuning
Abstract
We present Streamo, a real-time streaming video LLM that serves as a general-purpose interactive assistant. Unlike existing online video models that focus narrowly on question answering or captioning, Streamo performs a broad spectrum of streaming video tasks, including real-time narration, action understanding, event captioning, temporal event grounding, and time-sensitive question answering. To develop such versatility, we construct Streamo-Instruct-465K, a large-scale instruction-following dataset tailored for streaming video understanding. The dataset covers diverse temporal contexts and multi-task supervision, enabling unified training across heterogeneous streaming tasks. After training end-to-end on the instruction-following dataset through a streamlined pipeline, Streamo exhibits strong temporal reasoning, responsive interaction, and broad generalization across a variety of streaming benchmarks. Extensive experiments show that Streamo bridges the gap between offline video perception models and real-time multimodal assistants, making a step toward unified, intelligent video understanding in continuous video streams.
Multi-task annotation in Streamo-Instruct-465K.
Streamo's architecture. Streaming video data is organized into an interleaved, multi-turn dialogue struc ture that directly integrates a response-state token into the data sequence, enabling end-to-end parallel training.
Comparison with state-of-the-art on OVO-Bench.
Caption Task Demo Video
Cooking Demo Video
Streamo Paper
BibTeX
@article{xia2025streaming,
title={Streaming Video Instruction Tuning},
author={Xia, Jiaer and Chen, Peixian and Zhang, Mengdan and Sun, Xing and Zhou, Kaiyang},
journal={arXiv preprint arXiv:2512.21334},
year={2025}
}