Large language models (LLMs) have achieved remarkable success in many natural language tasks but still struggle with complex, multi-step reasoning, particularly across diverse disciplines. Existing reasoning datasets often either lack disciplinary breadth or the structural depth necessary to elicit robust reasoning behaviors. We propose DESIGNER: a DESIGN-logic-guidEd Reasoning data synthesis pipeline that leverages naturally available, extensive raw documents (book corpus and web corpus) to generate multidisciplinary challenging questions. A core innovation of our approach is the introduction of a Design Logic concept, which mimics the question-creation process of human educators. We use LLMs to reverse-engineer and abstract over 120,000 design logics from existing questions across various disciplines. By matching these design logics with disciplinary source materials, we are able to create reasoning questions that far surpass the difficulty and diversity of existing datasets. Based on this pipeline, we synthesized two large-scale reasoning datasets that span 75 disciplines: Design-Logic-Reasoning-Book (DLR-Book), containing 3.04 million challenging questions synthesized from the book corpus, and Design-Logic-Reasoning-Web (DLR-Web), with 1.66 million challenging questions from the web corpus. Our data analysis demonstrates that the questions synthesized by our method exhibit substantially greater difficulty and diversity than those in the baseline datasets. We validate the effectiveness of these datasets by conducting SFT experiments on the Qwen3-8B-Base and Qwen3-4B-Base models. The results show that our dataset significantly outperforms existing multidisciplinary datasets of the same volume. Training with the full datasets further enables the models to surpass the multidisciplinary reasoning performance of the official Qwen3-8B and Qwen3-4B models.
View on arXiv