OmniEdu fine-tunes 4B/9B/27B models on 69,999 capability-oriented examples, beating each base model across curriculum grounding, K–12 problem solving, and pedagogical tutoring
Researchers from Peking University, the University of the Chinese Academy of Sciences, and Zhongguancun Academy built OmniEdu, an open family of K–12 learning-and-teaching foundation models, trained with a capability-oriented instruction corpus organized around subject competence, curriculum grounding, diagnostic reasoning, and pedagogical action and scaffolding (69,999 examples and 15.96M supervised response tokens, of which 60,951 examples and about 12.0M tokens are education-specific), fine-tuning 4B, 9B, and 27B backbones with full-parameter supervised fine-tuning and reporting consistent gains at every scale on curriculum-grounding, K–12 problem-solving, and pedagogical-tutoring benchmarks, with OmniEdu-27B reaching 63.12% EM / 76.69% F1 on K12-Bench, 85.89% on MathFish, 86.