史上最大高质量科学推理后训练数据集开源，快速让Qwen3等变“科学家”

Core Viewpoint - The release of MegaScience, a large-scale open-source dataset for scientific reasoning, aims to enhance the training and evaluation of general artificial intelligence systems in scientific domains, addressing the lack of high-quality training data in scientific reasoning tasks [1][9][15]. Group 1: Dataset Overview - MegaScience consists of approximately 1.25 million question-answer pairs across various disciplines, including biology, chemistry, computer science, economics, mathematics, medicine, and physics [1][15]. - The dataset has been downloaded over 4,600 times within a week of its release and ranks fourth on the HuggingFace Datasets Trending list, indicating significant interest from the academic and industrial research communities [7]. Group 2: Performance and Evaluation - Models trained on MegaScience significantly outperform corresponding official Instruct models in scientific reasoning tasks, demonstrating the dataset's effectiveness [3][16]. - The dataset exhibits good scalability, with performance gains becoming more pronounced as the size of the base models increases [3][16]. Group 3: Challenges Addressed - Existing scientific reasoning datasets face challenges such as unreliable benchmark evaluations, inadequate decontamination processes, low-quality reference answers, and superficial knowledge distillation [10][11][13]. - MegaScience addresses these challenges through a systematic approach, including the development of a comprehensive scientific reasoning evaluation framework and rigorous data decontamination processes [13][15]. Group 4: Data Construction Process - The construction of MegaScience involved collecting data from multiple public datasets, implementing deduplication and decontamination strategies, and applying various data selection techniques to ensure high-quality outputs [27][28][30]. - The TextbookReasoning dataset, a component of MegaScience, was created using a fully automated process that extracted and refined question-answer pairs from approximately 120,000 university-level textbooks [14][19][20]. Group 5: Evaluation Framework - The evaluation framework for MegaScience includes 15 representative benchmark tasks designed to comprehensively assess the scientific reasoning capabilities of language models [37][39]. - The framework optimizes answer extraction processes to enhance the accuracy of evaluation results, ensuring a fair comparison between models [39][41]. Group 6: Future Prospects - Future research may explore the integration of reinforcement learning with MegaScience to further enhance scientific reasoning capabilities, leveraging the high-quality reference answers provided by the dataset [47][48].