SAIL-VL2 Technical Report
Authors: name: Weijie Yin, name: Yongjie Ye, name: Fangxun Shu, name: Yue Liao, name: Zijian Kang, name: Hongyuan Dong, name: Haiyang Yu, name: Dingkang Yang, name: Jiacong Wang, name: Han Wang, name: Wenzhuo Liu, name: Xiao Liang, name: Shuicheng Yan, name: Chao Feng

We introduce SAIL-VL2, an open-suite vision-language foundation model (LVM)
for comprehensive multimodal understanding and reasoning. As the successor to
SAIL-VL, SAIL-VL2 achieves state-of-the-art performance at the 2B and 8B
parameter scales across diverse image and video benchmarks, demonstrating
strong capabilities from fine-grained perception to complex reasoning. Three
core innovations drive its effectiveness. First, a large-scale data curation
pipeline with scoring and filtering strategies enhances both quality and
distribution across captioning, OCR, QA, and video data, improving training
efficiency. Second, a progressive training framework begins with a powerful
pre-trained vision encoder (SAIL-ViT), advances through multimodal
pre-training, and culminates in a thinking-fusion SFT-RL hybrid paradigm that
systematically strengthens model capabilities. Third, architectural advances
extend beyond dense LLMs to efficient sparse Mixture-of-Experts (MoE) designs.
With these contributions, SAIL-VL2 demonstrates competitive performance across
106 datasets and achieves state-of-the-art results on challenging reasoning
benchmarks such as MMMU and MathVista. Furthermore, on the OpenCompass
leaderboard, SAIL-VL2-2B ranks first among officially released open-source
models under the 4B parameter scale, while serving as an efficient and
extensible foundation for the open-source multimodal community.