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
Published: 2025-09-17 14:34:02

Abstract: 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.