---
title: "Who cites OpenMathInstruct-1: A 1.8 Million — 89 citing papers"
description: "89 papers of the Elamur Research library cite «OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset», found in their…"
canonical: "https://research.elamur.ai/free/en/papers/toshniwal-2024-openmathinstruct-1-1-8-million/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/toshniwal-2024-openmathinstruct-1-1-8-million/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [OpenMathInstruct-1: A 1.8 Million Math…](https://md.research.elamur.ai/free/en/papers/toshniwal-2024-openmathinstruct-1-1-8-million/) / cited by

# 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»](https://md.research.elamur.ai/free/en/papers/toshniwal-2024-openmathinstruct-1-1-8-million/)

1. [**Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context arXiv:2403.05530**Gemini Team, Petko Georgiev, Ving Ian Lei, et al.*2024 · 1,589 citations*](https://md.research.elamur.ai/free/en/papers/gemini-team-2024-gemini-1-5-unlocking-multimodal/)
2. [**WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct arXiv:2308.09583**Haipeng Luo, Qingfeng Sun, Can Xu, et al.*2023 · 372 citations*](https://md.research.elamur.ai/free/en/papers/luo-2023-wizardmath-empowering-mathematical-reasoning-large/)
3. **[Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-towards-reasoning-era-survey-long/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Libo Qin, Jinhao Liu, et al.*2025 · 176 citations*
4. **[Iterative Reasoning Preference Optimization](https://md.research.elamur.ai/free/en/papers/pang-2024-iterative-reasoning-preference-optimization/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Richard Yuanzhe Pang, Weizhe Yuan, Kyunghyun Cho, et al.*2024 · 153 citations*
5. **[MAmmoTH2: Scaling Instructions from the Web](https://md.research.elamur.ai/free/en/papers/yue-2024-mammoth2-scaling-instructions-web/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Xiang Yue, Tuney Zheng, Ge Zhang, et al.*2024 · 96 citations*
6. [**Advancing LLM Reasoning Generalists with Preference Trees arXiv:2404.02078**Lifan Yuan, Ganqu Cui, Hanbin Wang, et al.*2024 · 94 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2024-advancing-llm-reasoning-generalists-preference/)
7. [**OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data arXiv:2410.01560**Shubham Toshniwal, Wei Du, Ivan Moshkov, et al.*2024 · 85 citations*](https://md.research.elamur.ai/free/en/papers/toshniwal-2024-openmathinstruct-2-accelerating-ai-math/)
8. [**DART-Math: Difficulty-Aware Rejection Tuning for Mathematical Problem-Solving arXiv:2407.13690**Yuxuan Tong, Xiwen Zhang, Rui Wang, et al.*2024 · 81 citations*](https://md.research.elamur.ai/free/en/papers/tong-2024-dart-math-difficulty-aware-rejection/)
9. [**DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning arXiv:2504.11456**Zhiwei He, Tian Liang, Jiahao Xu, et al.*2025 · 78 citations*](https://md.research.elamur.ai/free/en/papers/he-2025-deepmath-103k-large-scale-challenging/)
10. **[Recursive Introspection: Teaching Language Model Agents How to Self-Improve](https://md.research.elamur.ai/free/en/papers/qu-2024-recursive-introspection-teaching-language-model/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yuxiao Qu, Tianjun Zhang, Naman Garg, et al.*2024 · 75 citations*
11. [**EuroLLM-9B: Technical Report arXiv:2506.04079**Pedro Henrique Martins, Joao Alves, Patrick Fernandes, et al.*2025 · 45 citations*](https://md.research.elamur.ai/free/en/papers/martins-2025-eurollm-9b-technical-report/)
12. **[SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature](https://md.research.elamur.ai/free/en/papers/wadden-2025-sciriff-resource-enhance-language-model/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**David Wadden, Kejian Shi, Jacob Morrison, et al.*2025 · 27 citations*
13. **[A Survey of Post-Training Scaling in Large Language Models](https://md.research.elamur.ai/free/en/papers/lai-2025-survey-post-training-scaling-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/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 On arXiv:2407.08348**Liang Zeng, Liangjun Zhong, Liang Zhao, et al.*2024 · 14 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2024-skywork-math-data-scaling-laws/)
15. [**Maximize Your Data's Potential: Enhancing LLM Accuracy with Two-Phase Pretraining arXiv:2412.15285**Steven Feng, Shrimai Prabhumoye, Kezhi Kong, et al.*2024 · 12 citations*](https://md.research.elamur.ai/free/en/papers/feng-2024-maximize-data-s-potential-enhancing/)
16. [**Leveraging Online Olympiad-Level Math Problems for LLMs Training and Contamination-Resistant Evaluation arXiv:2501.14275**Sadegh Mahdavi, Muchen Li, Kaiwen Liu, et al.*2025 · 10 citations*](https://md.research.elamur.ai/free/en/papers/mahdavi-2025-leveraging-online-olympiad-level-math/)
17. **[Nemotron-CrossThink: Scaling Self-Learning beyond Math Reasoning](https://md.research.elamur.ai/free/en/papers/akter-2026-nemotron-crossthink-scaling-self-learning/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Syeda Nahida Akter, Shrimai Prabhumoye, Matvei Novikov, et al.*2026 · 9 citations*
18. **[FastMCTS: A Simple Sampling Strategy for Data Synthesis](https://md.research.elamur.ai/free/en/papers/li-2025-fastmcts-simple-sampling-strategy-data/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Peiji Li, Kai Lv, Yunfan Shao, et al.*2025 · 8 citations*
19. **[Genius: A Generalizable and Purely Unsupervised Self-Training Framework For Advanced Reasoning](https://md.research.elamur.ai/free/en/papers/xu-2025-genius-generalizable-purely-unsupervised-self/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/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 Problems arXiv:2410.18921**A M Muntasir Rahman, Junyi Ye, Wei Yao, et al.*2024 · 6 citations*](https://md.research.elamur.ai/free/en/papers/rahman-2024-blind-solvers-logical-thinkers-benchmarking/)
21. **[Evaluating Numeracy of Language Models as a Natural Language Inference Task](https://md.research.elamur.ai/free/en/papers/mahendra-2025-evaluating-numeracy-language-models-as/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Rahmad Mahendra, Damiano Spina, Lawrence Cavedon, et al.*2025 · 4 citations*
22. **[Observations and Remedies for Large Language Model Bias in Self-Consuming Performative Loop](https://md.research.elamur.ai/free/en/papers/wang-2026-observations-remedies-large-language-model/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yaxuan Wang, Zhongteng Cai, Yujia Bao, et al.*2026*
23. **[RV-Syn: Rational and Verifiable Mathematical Reasoning Data Synthesis based on Structured Function Library](https://md.research.elamur.ai/free/en/papers/wang-2026-rv-syn-rational-verifiable-mathematical/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jiapeng Wang, Jinhao Jiang, Zhiqiang Zhang, et al.*2026*

66 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/toshniwal-2024-openmathinstruct-1-1-8-million/) — OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset