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

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.