Computer Science / Artificial Intelligence

Logics-STEM: Empowering LLM Reasoning via Failure-Driven Post-Training and Document Knowledge Enhancement

February 09, 2026

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Title: Logics-STEM: Empowering LLM Reasoning via Failure-Driven Post-Training and Document Knowledge Enhancement
ArXiv ID: 2601.01562
Date: 2026-01-04
Authors: Mingyu Xu, Cheng Fang, Keyue Jiang, Yuqian Zheng, Yanghua Xiao, Baojian Zhou, Qifang Zhao, Suhang Zheng, Xiuwen Zhu, Jiyang Tang, Yongchi Zhao, Yijia Luo, Zhiqi Bai, Yuchi Xu, Wenbo Su, Wei Wang, Bing Zhao, Lin Qu, Xiaoxiao Xu

📝 Abstract

We present Logics-STEM, a state-of-the-art reasoning model fine-tuned on Logics-STEM-SFT-Dataset, a high-quality and diverse dataset at 7.2M scale that represents one of the largest-scale open-source long chain-of-thought corpora. Logics-STEM targets reasoning tasks in the domains of Science, Technology, Engineering, and Mathematics (STEM), and exhibits exceptional performance on STEM-related benchmarks with an average improvement of 4.68% over the next-best model at 8B scale. We attribute the gains to our data-algorithm co-design engine, where they are jointly optimized to fit a gold-standard distribution behind reasoning. Data-wise, the Logics-STEM-SFT-Dataset is constructed from a meticulously designed data curation engine with 5 stages to ensure the quality, diversity, and scalability, including annotation, deduplication, decontamination, distillation, and stratified sampling. Algorithm-wise, our failure-driven posttraining framework leverages targeted knowledge retrieval and data synthesis around model failure regions i...

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Logics-STEM: Empowering LLM Reasoning via Failure-Driven Post-Training and Document Knowledge Enhancement

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