Innovator-VL: A Multimodal Large Language Model for Scientific Discovery
We present Innovator-VL, a scientific multimodal large language model designed to advance understanding and reasoning across diverse scientific domains while maintaining excellent performance on general vision tasks. Contrary to the trend of relying on massive domain-specific pretraining and opaque pipelines, our work demonstrates that principled training design and transparent methodology can yield strong scientific intelligence with substantially reduced data requirements. (i) First, we provide a fully transparent, end-to-end reproducible training pipeline, covering data collection, cleaning, preprocessing, supervised fine-tuning, reinforcement learning, and evaluation, along with detailed optimization recipes. This facilitates systematic extension by the community. (ii) Second, Innovator-VL exhibits remarkable data efficiency, achieving competitive performance on various scientific tasks using fewer than five million curated samples without large-scale pretraining. These results highlight that effective reasoning can be achieved through principled data selection rather than indiscriminate scaling. (iii) Third, Innovator-VL demonstrates strong generalization, achieving competitive performance on general vision, multimodal reasoning, and scientific benchmarks. This indicates that scientific alignment can be integrated into a unified model without compromising general-purpose capabilities. Our practices suggest that efficient, reproducible, and high-performing scientific multimodal models can be built even without large-scale data, providing a practical foundation for future research.
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Zichen Wen
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2026-02-03T10:09:56.690Z
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Zichen Wen
zichenwen
user
zichenwen
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Boxue Yang
claimed_verified
2026-01-29T09:17:12.982Z
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BoxueYang
Boxue
user
Boxue
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Shuang Chen
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Yaojie Zhang
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Yuhang Han
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Junlong Ke
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Cong Wang
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Yicheng Fu
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Jiawang Zhao
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Jiangchao Yao
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Xi Fang
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Zhen Wang
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Henxing Cai
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Lin Yao
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Zhifeng Gao
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Yanhui Hong
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Nang Yuan
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Yixuan Li
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Guojiang Zhao
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Haoyi Tao
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Nan Wang
69798298df44b75fa47e47be
Han Lyu
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Guolin Ke
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Ning Liao
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Xiaoxing Wang
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Kai Chen
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Zhiyu Li
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Feiyu Xiong
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Sihan Hu
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Kun Chen
69798298df44b75fa47e47c7
Yanfeng Wang
69798298df44b75fa47e47c8
Weinan E
69798298df44b75fa47e47c9
Linfeng Zhang
69798298df44b75fa47e47ca
Linfeng Zhang
Innovator-VL: A Multimodal Large Language Model for Scientific Discovery
We present Innovator-VL, a scientific multimodal large language model designed to advance understanding and reasoning across diverse scientific domains while maintaining excellent performance on general vision tasks. Contrary to the trend of relying on massive domain-specific pretraining and opaque pipelines, our work demonstrates that principled training design and transparent methodology can yield strong scientific intelligence with substantially reduced data requirements. (i) First, we provide a fully transparent, end-to-end reproducible training pipeline, covering data collection, cleaning, preprocessing, supervised fine-tuning, reinforcement learning, and evaluation, along with detailed optimization recipes. This facilitates systematic extension by the community. (ii) Second, Innovator-VL exhibits remarkable data efficiency, achieving competitive performance on various scientific tasks using fewer than five million curated samples without large-scale pretraining. These results highlight that effective reasoning can be achieved through principled data selection rather than indiscriminate scaling. (iii) Third, Innovator-VL demonstrates strong generalization, achieving competitive performance on general vision, multimodal reasoning, and scientific benchmarks. This indicates that scientific alignment can be integrated into a unified model without compromising general-purpose capabilities. Our practices suggest that efficient, reproducible, and high-performing scientific multimodal models can be built even without large-scale data, providing a practical foundation for future research.
Innovator-VL demonstrates that principled training design and transparent methodology can achieve strong scientific intelligence with reduced data requirements while maintaining general vision performance.
multimodal large language model scientific multimodal large language model end-to-end reproducible training pipeline supervised fine-tuning reinforcement learning scientific reasoning data efficiency principled data selection generalization scientific alignment