OmniEdu: Open Foundation Models for Learning and Teaching

Educational foundation models must do more than produce correct answers: they must understand where a problem sits in a curriculum, diagnose why a learner is struggling, and choose an appropriate instructional response. Existing educational language models often specialize in either subject problem solving or tutoring, while their training mixtures are commonly organized by source or task and do not explicitly balance these capabilities. We present OmniEdu, an open family of foundation models for K–12 learning and teaching, trained with a capability-oriented instruction-tuning corpus. The corpus combines more than 100 educational resources and general instruction sources and organizes supervision around four complementary capabilities: subject competence, curriculum grounding, diagnostic reasoning, and pedagogical action and scaffolding. A multi-stage pipeline performs deterministic cleaning, semantic auditing and rewriting, task-specific quality scoring, token-budgeted diversity selection, and pedagogical instruction assignment, yielding 69,999 examples and 15.96M supervised response tokens, including 60,951 education-specific examples. We fine-tune 4B, 9B, and 27B models and evaluate them on curriculum-grounding, K–12 problem-solving, and pedagogical-tutoring benchmarks, with auxiliary tests of general capability. Across model scales, education-oriented tuning consistently improves all three educational capability groups. In particular, OmniEdu-27B reaches 63.12% EM / 76.69% F1 on K12-Bench, 85.89% on MathFish, 86.95% on EDUMATH, 78.74% on MathTutorBench's Scaffold setting, and the best Teaching average of 3.02 on LongTutor among the evaluated models. These results show that carefully curated, capability-balanced supervision can turn a general language model into a stronger educational system that not only solves problems, but also understands curriculum structure and supports effective teaching interactions.