This dataset consisted of millions of high-quality Chain-of-Thought (CoT) questions are derived from authoritative sources and include questions and answers. These datasets undergo rigorous processing steps such as question screening, entry, duplicate checking, solving, review, and proofreading, followed by strict quality control to form standardized question banks.
Competition-level Mathematics, Physics Reasoning Corpus
This dataset is for AI models to train to learn to extract critical information from problem statements and methodically derive solutions. This type of dataset proves particularly valuable for developing automated question-answering systems and AI applications requiring sophisticated reasoning capabilities.
University-level Business, Law, Medicine Reasoning Corpus
This dataset is for AI models to train to learn to extract critical information from problem statements and methodically derive solutions. This type of dataset proves particularly valuable for developing automated question-answering systems and AI applications requiring sophisticated reasoning capabilities.
University-level Mathematics, Physics, Chemistry, Computer Science Reasoning Corpus
This dataset is for AI models to train to learn to extract critical information from problem statements and methodically derive solutions. This type of dataset proves particularly valuable for developing automated question-answering systems and AI applications requiring sophisticated reasoning capabilities.
K12 (Primary/Junior/Senior High) Testing Questions Across all Subjects
This dataset is for AI models to train to learn to extract critical information from problem statements and methodically derive solutions. This type of dataset proves particularly valuable for developing automated question-answering systems and AI applications requiring sophisticated reasoning capabilities
