SciReasoner：跨学科科学推理基础构建

摘要

我们提出了一种科学推理基础模型，该模型将自然语言与异构的科学表示对齐。该模型在包含科学文本、纯序列及序列-文本对的206B标记语料库上进行预训练，随后通过40M指令的监督微调（SFT）进行对齐，采用退火冷启动引导以激发长链思维，并结合任务特定的奖励塑造进行强化学习，从而培养出深思熟虑的科学推理能力。该模型支持四大能力家族，覆盖工作流中的多达103项任务：(i) 文本与科学格式间的忠实转换，(ii) 文本/知识抽取，(iii) 属性预测，(iv) 属性分类，(v) 无条件与条件序列生成及设计。相较于专业系统，我们的方法拓宽了指令覆盖范围，提升了跨领域泛化能力，并增强了保真度。我们详细阐述了数据整理与训练过程，并展示了跨学科学习如何强化迁移与下游任务的可靠性。该模型、指令调优数据集及评估代码已开源，访问地址为https://huggingface.co/SciReason 和 https://github.com/open-sciencelab/SciReason。

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.

SciReasoner：跨学科科学推理基础构建

SciReasoner: Laying the Scientific Reasoning Ground Across Disciplines

摘要

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