# OmniEdu: Open Foundation Models for Learning and Teaching

arXiv: https://arxiv.org/abs/2609.23088 · HTML: https://mlsanity.com/paper/2609.23088/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 36 references that are in the map.
- First posted on arXiv 19 Sep 2026.

## Builds on (most cited first, 27 of 27)

- [GPQA: A Graduate-Level Google-Proof Q&A Benchmark](https://mlsanity.com/paper/2311.12022/) (2023)
- [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://mlsanity.com/paper/2209.09513/) (2022)
- [Instruction-Following Evaluation for Large Language Models](https://mlsanity.com/paper/2311.07911/) (2023)
- [LESS: Selecting Influential Data for Targeted Instruction Tuning](https://mlsanity.com/paper/2402.04333/) (2024)
- [MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark](https://mlsanity.com/paper/2409.02813/) (2024)
- [What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning](https://mlsanity.com/paper/2312.15685/) (2023)
- [Evaluating the Performance of Large Language Models on GAOKAO Benchmark](https://mlsanity.com/paper/2305.12474/) (2023)
- [M3Exam: A Multilingual, Multimodal, Multilevel Benchmark for Examining Large Language Models](https://mlsanity.com/paper/2306.05179/) (2023)
- [EduChat: A Large-Scale Language Model-based Chatbot System for Intelligent Education](https://mlsanity.com/paper/2308.02773/) (2023)
- [Unifying AI Tutor Evaluation: An Evaluation Taxonomy for Pedagogical Ability Assessment of LLM-Powered AI Tutors](https://mlsanity.com/paper/2412.09416/) (2024)
- [MathTutorBench: A Benchmark for Measuring Open-ended Pedagogical Capabilities of LLM Tutors](https://mlsanity.com/paper/2502.18940/) (2025)
- [EXAMS-V: A Multi-Discipline Multilingual Multimodal Exam Benchmark for Evaluating Vision Language Models](https://mlsanity.com/paper/2403.10378/) (2024)
- [CMMU: A Benchmark for Chinese Multi-modal Multi-type Question Understanding and Reasoning](https://mlsanity.com/paper/2401.14011/) (2024)
- [Instruction Tuning with Human Curriculum](https://mlsanity.com/paper/2310.09518/) (2023)
- [E-EVAL: A Comprehensive Chinese K-12 Education Evaluation Benchmark for Large Language Models](https://mlsanity.com/paper/2401.15927/) (2024)
- [TutorBench: A Benchmark To Assess Tutoring Capabilities Of Large Language Models](https://mlsanity.com/paper/2510.02663/) (2025)
- [K12Vista: Exploring the Boundaries of MLLMs in K-12 Education](https://mlsanity.com/paper/2506.01676/) (2025)
- [Confucius3-Math: A Lightweight High-Performance Reasoning LLM for Chinese K-12 Mathematics Learning](https://mlsanity.com/paper/2506.18330/) (2025)
- [MDK12-Bench: A Comprehensive Evaluation of Multimodal Large Language Models on Multidisciplinary Exams](https://mlsanity.com/paper/2508.06851/) (2025)
- [KnowShiftQA: How Robust are RAG Systems when Textbook Knowledge Shifts in K-12 Education?](https://mlsanity.com/paper/2412.08985/) (2024)
- [GAOKAO-Eval: Does high scores truly reflect strong capabilities in LLMs?](https://mlsanity.com/paper/2412.10056/) (2024)
- [EduAdapt: A Question Answer Benchmark Dataset for Evaluating Grade-Level Adaptability in LLMs](https://mlsanity.com/paper/2510.17389/) (2025)
- [SafeTutors: Benchmarking Pedagogical Safety in AI Tutoring Systems](https://mlsanity.com/paper/2603.17373/) (2026)
- [CJEval: A Benchmark for Assessing Large Language Models Using Chinese Junior High School Exam Data](https://mlsanity.com/paper/2409.16202/) (2024)
- [MMTutorBench: The First Multimodal Benchmark for AI Math Tutoring](https://mlsanity.com/paper/2510.23477/) (2025)
- [From Solver to Tutor: Evaluating the Pedagogical Intelligence of LLMs with KMP-Bench](https://mlsanity.com/paper/2603.02775/) (2026)
- [K12-KGraph: A Curriculum-Aligned Knowledge Graph for Benchmarking and Training Educational LLMs](https://mlsanity.com/paper/2605.09635/) (2026)

## Abstract (arXiv)

Educational foundation models must solve problems, understand curriculum structure, diagnose learner difficulties, and provide appropriate instructional support. Existing educational language models often focus on either problem solving or tutoring, with training mixtures organized by source or task rather than capability. We present OmniEdu, an open family of foundation models for K-12 learning and teaching. Its instruction-tuning corpus combines over 100 educational resources and general instruction sources, organized around four capabilities: subject competence, curriculum grounding, diagnostic reasoning, and pedagogical action and scaffolding. Our pipeline integrates deterministic cleaning, semantic auditing and rewriting, task-specific quality scoring, token-budgeted diversity selection, and pedagogical instruction assignment. It yields 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 curriculum grounding, K-12 problem solving, and pedagogical tutoring, alongside general capability. Education-oriented tuning consistently improves all three educational benchmark groups across model scales. OmniEdu-27B achieves 63.12% EM and 76.69% F1 on K12-Bench, 85.89% on MathFish, 86.95% on EDUMATH, and 78.74% in MathTutorBench's Scaffold setting. It also achieves the highest Teaching average on LongTutor among the evaluated models, at 3.02. These results demonstrate the value of curated, capability-balanced supervision for adapting general language models to educational tasks spanning problem solving, curriculum understanding, and instructional support.