SciReasoner: Laying the Scientific Reasoning Ground Across Disciplines
We present a scientific reasoning foundation model that aligns natural
language with heterogeneous scientific representations. The model is pretrained
on a 206B-token corpus spanning scientific text, pure sequences, and
sequence-text pairs, then aligned via SFT on 40M instructions, annealed
cold-start bootstrapping to elicit long-form chain-of-thought, and
reinforcement learning with task-specific reward shaping, which instills
deliberate scientific reasoning. It supports four capability families, covering
up to 103 tasks across workflows: (i) faithful translation between text and
scientific formats, (ii) text/knowledge extraction, (iii) property prediction,
(iv) property classification, (v) unconditional and conditional sequence
generation and design. Compared with specialist systems, our approach broadens
instruction coverage, improves cross-domain generalization, and enhances
fidelity. We detail data curation and training and show that cross-discipline
learning strengthens transfer and downstream reliability. The model, instruct
tuning datasets and the evaluation code are open-sourced at
https://huggingface.co/SciReason and
https://github.com/open-sciencelab/SciReason.
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Yizhou Wang
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2025-09-26T12:19:52.127Z
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Yizhou Wang
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Chen Tang
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Jiaqi Liu
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Jianyu Wu
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Jianyu Wu
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Pengze Li
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Encheng Su
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Lintao Wang
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Guohang Zhuang
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Guohang Zhuang
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Yuchen Ren
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Ben Fei
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Ming Hu
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Xin Chen
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Xiangyu Yue
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Zhenfei Yin
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Zhenfei Yin
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Jiamin Wu
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Qihao Zheng
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Yuhao Zhou
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Huihui Xu
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Chenglong Ma
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2025-09-26T16:03:53.160Z
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Chenglong Ma
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Yan Lu
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Wenlong Zhang
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Chunfeng Song
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Philip Torr
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Shixiang Tang
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SHIXIANG TANG
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Xinzhu Ma
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xinzhuma
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Wanli Ouyang
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Lei Bai
SciReasoner: Laying the Scientific Reasoning Ground Across Disciplines
We present a scientific reasoning foundation model that aligns natural
language with heterogeneous scientific representations. The model is pretrained
on a 206B-token corpus spanning scientific text, pure sequences, and
sequence-text pairs, then aligned via SFT on 40M instructions, annealed
cold-start bootstrapping to elicit long-form chain-of-thought, and
reinforcement learning with task-specific reward shaping, which instills
deliberate scientific reasoning. It supports four capability families, covering
up to 103 tasks across workflows: (i) faithful translation between text and
scientific formats, (ii) text/knowledge extraction, (iii) property prediction,
(iv) property classification, (v) unconditional and conditional sequence
generation and design. Compared with specialist systems, our approach broadens
instruction coverage, improves cross-domain generalization, and enhances
fidelity. We detail data curation and training and show that cross-discipline
learning strengthens transfer and downstream reliability. The model, instruct
tuning datasets and the evaluation code are open-sourced at
https://huggingface.co/SciReason and
https://github.com/open-sciencelab/SciReason.
A scientific reasoning foundation model pre-trained on diverse scientific data supports multiple tasks and enhances cross-domain generalization and fidelity through specialized training techniques.
scientific reasoning foundation model SFT cold-start bootstrapping reinforcement learning task-specific reward shaping chain-of-thought translation text/knowledge extraction property prediction property classification sequence generation sequence design cross-discipline learning transfer downstream reliability