标题： ChemDFM-R：一个增强原子化化学知识的化学推理大语言模型 显示英文标题

标题： ChemDFM-R: An Chemical Reasoner LLM Enhanced with Atomized Chemical Knowledge

摘要： 虽然大型语言模型（LLMs）取得了显著进展，但它们在化学等科学领域中的应用仍受到浅层领域理解和有限推理能力的阻碍。 在本工作中，我们专注于化学这一特定领域，并开发了一个化学推理大型语言模型，ChemDFM-R。我们首先构建了一个全面的原子化知识点数据集，以增强模型对化学基本原理和逻辑结构的理解。 然后，我们提出了一种混合来源的知识蒸馏策略，将专家整理的知识与通用领域的推理技能相结合，随后进行领域特定的强化学习以提升化学推理能力。 在多种化学基准测试中进行的实验表明，ChemDFM-R在提供可解释的、基于理由的输出的同时，达到了最先进的性能。 进一步的案例研究展示了显式推理链如何显著提高模型在现实世界人机协作场景中的可靠性、透明度和实用性。 显示英文摘要

摘要： While large language models (LLMs) have achieved impressive progress, their application in scientific domains such as chemistry remains hindered by shallow domain understanding and limited reasoning capabilities. In this work, we focus on the specific field of chemistry and develop a Chemical Reasoner LLM, ChemDFM-R. We first construct a comprehensive dataset of atomized knowledge points to enhance the model's understanding of the fundamental principles and logical structure of chemistry. Then, we propose a mix-sourced distillation strategy that integrates expert-curated knowledge with general-domain reasoning skills, followed by domain-specific reinforcement learning to enhance chemical reasoning. Experiments on diverse chemical benchmarks demonstrate that ChemDFM-R achieves cutting-edge performance while providing interpretable, rationale-driven outputs. Further case studies illustrate how explicit reasoning chains significantly improve the reliability, transparency, and practical utility of the model in real-world human-AI collaboration scenarios.