跨学科科学推理大模型，能理解并生成各类科学内容。
SciReasoner: Laying the Scientific Reasoning Ground Across Disciplines
- 用2060亿文本训练，结合指令微调与强化学习，实现科学思维链推理。
- 支持103项任务，跨领域泛化能力优于专用系统，生成更准确。
- 适合科研人员、教育者，用于科学内容生成与知识提取。
我们提出一种科学推理基础模型，将自然语言与异构科学表示对齐。模型在包含2060亿个标记的语料库上预训练，涵盖科学文本、纯序列及序列-文本对，随后通过4000万条指令进行监督微调，并采用渐进式冷启动自举方法激发长链思维过程，再结合任务特定奖励塑造的强化学习，赋予模型刻意的科学推理能力。该模型支持五大能力族，覆盖最多达103项任务，涵盖工作流中的：(i) 文本与科学格式间的忠实转换，(ii) 文本/知识提取，(iii) 属性预测，(iv) 属性分类，(v) 无条件与有条件序列生成与设计。相较于专用系统，本方法扩大了指令覆盖范围，提升了跨域泛化能力，并增强输出保真度。我们详述数据构建与训练流程，并证明跨学科学习可提升迁移性能与下游可靠性。模型、指令微调数据集与评估代码已开源至 https://huggingface.co/SciReason 及 https://github.com/open-sciencelab/SciReason。
原文摘要 · Abstract (English)
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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