🔗 Who cites «OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset»

89 papers of our library cite it (of the 149,439 whose bibliographies we read) · 23 with a page here, the most cited first · ← back to «OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset»

1. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextarXiv:2403.05530Gemini Team, Petko Georgiev, Ving Ian Lei, et al.2024 · 1,589 citations
2. WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-InstructarXiv:2308.09583Haipeng Luo, Qingfeng Sun, Can Xu, et al.2023 · 372 citations
3. Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language ModelsScience China Information SciencesQiguang Chen, Libo Qin, Jinhao Liu, et al.2025 · 176 citations
4. Iterative Reasoning Preference OptimizationConference on Neural Information Processing Systems (NeurIPS)Richard Yuanzhe Pang, Weizhe Yuan, Kyunghyun Cho, et al.2024 · 153 citations
5. MAmmoTH2: Scaling Instructions from the WebConference on Neural Information Processing Systems (NeurIPS)Xiang Yue, Tuney Zheng, Ge Zhang, et al.2024 · 96 citations
6. Advancing LLM Reasoning Generalists with Preference TreesarXiv:2404.02078Lifan Yuan, Ganqu Cui, Hanbin Wang, et al.2024 · 94 citations
7. OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction DataarXiv:2410.01560Shubham Toshniwal, Wei Du, Ivan Moshkov, et al.2024 · 85 citations
8. DART-Math: Difficulty-Aware Rejection Tuning for Mathematical Problem-SolvingarXiv:2407.13690Yuxuan Tong, Xiwen Zhang, Rui Wang, et al.2024 · 81 citations
9. DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing ReasoningarXiv:2504.11456Zhiwei He, Tian Liang, Jiahao Xu, et al.2025 · 78 citations
10. Recursive Introspection: Teaching Language Model Agents How to Self-ImproveConference on Neural Information Processing Systems (NeurIPS)Yuxiao Qu, Tianjun Zhang, Naman Garg, et al.2024 · 75 citations
11. EuroLLM-9B: Technical ReportarXiv:2506.04079Pedro Henrique Martins, Joao Alves, Patrick Fernandes, et al.2025 · 45 citations
12. SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific LiteratureConference on Empirical Methods in Natural Language Processing (EMNLP)David Wadden, Kejian Shi, Jacob Morrison, et al.2025 · 27 citations
13. A Survey of Post-Training Scaling in Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Hanyu Lai, Xiao Liu, Junjie Gao, et al.2025 · 18 citations
14. Skywork-Math: Data Scaling Laws for Mathematical Reasoning in Large Language Models — The Story Goes OnarXiv:2407.08348Liang Zeng, Liangjun Zhong, Liang Zhao, et al.2024 · 14 citations
15. Maximize Your Data's Potential: Enhancing LLM Accuracy with Two-Phase PretrainingarXiv:2412.15285Steven Feng, Shrimai Prabhumoye, Kezhi Kong, et al.2024 · 12 citations
16. Leveraging Online Olympiad-Level Math Problems for LLMs Training and Contamination-Resistant EvaluationarXiv:2501.14275Sadegh Mahdavi, Muchen Li, Kaiwen Liu, et al.2025 · 10 citations
17. Nemotron-CrossThink: Scaling Self-Learning beyond Math ReasoningConference of the European Chapter of the Association for Computational Linguistics (EACL)Syeda Nahida Akter, Shrimai Prabhumoye, Matvei Novikov, et al.2026 · 9 citations
18. FastMCTS: A Simple Sampling Strategy for Data SynthesisAnnual Meeting of the Association for Computational Linguistics (ACL)Peiji Li, Kai Lv, Yunfan Shao, et al.2025 · 8 citations
19. Genius: A Generalizable and Purely Unsupervised Self-Training Framework For Advanced ReasoningAnnual Meeting of the Association for Computational Linguistics (ACL)Fangzhi Xu, Hang Yan, Chang Ma, et al.2025 · 8 citations
20. From Blind Solvers to Logical Thinkers: Benchmarking LLMs' Logical Integrity on Faulty Mathematical ProblemsarXiv:2410.18921A M Muntasir Rahman, Junyi Ye, Wei Yao, et al.2024 · 6 citations
21. Evaluating Numeracy of Language Models as a Natural Language Inference TaskFindings of the Association for Computational LinguisticsRahmad Mahendra, Damiano Spina, Lawrence Cavedon, et al.2025 · 4 citations
22. Observations and Remedies for Large Language Model Bias in Self-Consuming Performative LoopAnnual Meeting of the Association for Computational Linguistics (ACL)Yaxuan Wang, Zhongteng Cai, Yujia Bao, et al.2026
23. RV-Syn: Rational and Verifiable Mathematical Reasoning Data Synthesis based on Structured Function LibraryFindings of the Association for Computational LinguisticsJiapeng Wang, Jinhao Jiang, Zhiqiang Zhang, et al.2026

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