LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal
  Training
Authors: name: Xiang An; name: Yin Xie; name: Kaicheng Yang; name: Wenkang Zhang; name: Xiuwei Zhao; name: Zheng Cheng; name: Yirui Wang; name: Songcen Xu; name: Changrui Chen; name: Chunsheng Wu; name: Huajie Tan; name: Chunyuan Li; name: Jing Yang; name: Jie Yu; name: Xiyao Wang; name: Bin Qin; name: Yumeng Wang; name: Zizhen Yan; name: Ziyong Feng; name: Ziwei Liu; name: Bo Li; name: Jiankang Deng
Published: 2025-09-28 05:52:55

Abstract: We present LLaVA-OneVision-1.5, a novel family of Large Multimodal Models
(LMMs) that achieve state-of-the-art performance with significantly reduced
computational and financial costs. Different from the existing works,
LLaVA-OneVision-1.5 provides an open, efficient, and reproducible framework for
building high-quality vision-language models entirely from scratch. The
LLaVA-OneVision-1.5 release comprises three primary components: (1) Large-Scale
Curated Datasets: We construct an 85M concept-balanced pretraining dataset
LLaVA-OneVision-1.5-Mid-Traning and a meticulously curated 26M instruction
dataset LLaVA-OneVision-1.5-Instruct, collectively encompassing 64B compressed
multimodal tokens. (2) Efficient Training Framework: We develop a complete
end-to-end efficient training framework leveraging an offline parallel data
packing strategy to facilitate the training of LLaVA-OneVision-1.5 within a
$16,000 budget. (3) State-of-the-art Performance: Experimental results
demonstrate that LLaVA-OneVision1.5 yields exceptionally competitive
performance across a broad range of downstream tasks. Specifically,
LLaVA-OneVision-1.5-8B outperforms Qwen2.5-VL-7B on 18 of 27 benchmarks, and
LLaVA-OneVision-1.5-4B surpasses Qwen2.5-VL-3B on all 27 benchmarks. We
anticipate releasing LLaVA-OneVision-1.5-RL shortly and encourage the community
to await further updates.