SAIL-VL2 Technical Report
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.
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Weijie Yin
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2025-09-21T13:13:15.236Z
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wjyin
user
Blue-skyyy
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Yongjie Ye
68cb69b55a7803ff3be42dcf
Fangxun Shu
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2025-09-18T13:26:50.598Z
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JackShu
user
Shuhuhuhu
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Yue Liao
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Zijian Kang
68cb69b55a7803ff3be42dd2
Hongyuan Dong
68cb69b55a7803ff3be42dd3
Haiyang Yu
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2025-09-18T13:26:48.039Z
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Haiyang Yu
user
hyyu20
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Dingkang Yang
68cb69b55a7803ff3be42dd5
Jiacong Wang
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2025-09-18T13:26:53.059Z
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wang
user
stormthunder
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Han Wang
68cb69b55a7803ff3be42dd7
Wenzhuo Liu
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Xiao Liang
68cb69b55a7803ff3be42dd9
Shuicheng Yan
68cb69b55a7803ff3be42dda
Chao Feng
SAIL-VL2 Technical Report
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.
SAIL-VL2, a vision-language foundation model, achieves state-of-the-art performance across diverse benchmarks through data curation, progressive training, and sparse MoE architecture.
vision-language foundation model SAIL-VL2 SAIL-VL parameter scales image and video benchmarks fine-grained perception complex reasoning large-scale data curation scoring and filtering strategies training efficiency progressive training framework powerful pre-trained vision encoder SAIL-ViT multimodal pre-training thinking-fusion SFT-RL hybrid paradigm dense LLMs efficient sparse Mixture-of-Experts MoE designs challenging reasoning benchmarks MMMU MathVista OpenCompass leaderboard