SciReasoner: Laying the Scientific Reasoning Ground Across Disciplines
Authors: name: Yizhou Wang; name: Chen Tang; name: Han Deng; name: Jiabei Xiao; name: Jiaqi Liu; name: Jianyu Wu; name: Jun Yao; name: Pengze Li; name: Encheng Su; name: Lintao Wang; name: Guohang Zhuang; name: Yuchen Ren; name: Ben Fei; name: Ming Hu; name: Xin Chen; name: Dongzhan Zhou; name: Junjun He; name: Xiangyu Yue; name: Zhenfei Yin; name: Jiamin Wu; name: Qihao Zheng; name: Yuhao Zhou; name: Huihui Xu; name: Chenglong Ma; name: Yan Lu; name: Wenlong Zhang; name: Chunfeng Song; name: Philip Torr; name: Shixiang Tang; name: Xinzhu Ma; name: Wanli Ouyang; name: Lei Bai
Published: 2025-09-25 17:52:06

Abstract: 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.