--- title: "Who cites Big-Math: A Large-Scale, — 43 citing papers" description: "43 papers of the Elamur Research library cite «Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language…" canonical: "https://research.elamur.ai/free/en/papers/albalak-2025-big-math-large-scale-high/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/albalak-2025-big-math-large-scale-high/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Big-Math: A Large-Scale, High-Quality Math…](https://md.research.elamur.ai/free/en/papers/albalak-2025-big-math-large-scale-high/) / cited by # Who cites «Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models» 43 papers of our library cite it (of the 149,439 whose bibliographies we read) · 22 with a page here, the most cited first · [← back to «Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models»](https://md.research.elamur.ai/free/en/papers/albalak-2025-big-math-large-scale-high/) 1. **[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* 2. **[Light-R1: Curriculum SFT, DPO and RL for Long COT from Scratch and Beyond](https://md.research.elamur.ai/free/en/papers/wen-2025-light-r1-curriculum-sft-dpo/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Liang Wen, Yunke Cai, Fenrui Xiao, et al.*2025 · 84 citations* 3. [**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/) 4. [**Seed-Prover: Deep and Broad Reasoning for Automated Theorem Proving arXiv:2507.23726**Luoxin Chen, Jinming Gu, Liankai Huang, et al.*2025 · 46 citations*](https://md.research.elamur.ai/free/en/papers/chen-2025-seed-prover-deep-broad-reasoning/) 5. **[From Problem-Solving to Teaching Problem-Solving: Aligning LLMs with Pedagogy using Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/jianu-2025-problem-solving-teaching-problem-solving/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**David Dinucu-Jianu, Jakub Macina, Nico Daheim, et al.*2025 · 19 citations* 6. [**Seed-Prover 1.5: Mastering Undergraduate-Level Theorem Proving via Learning from Experience arXiv:2512.17260**Jiangjie Chen, Wenxiang Chen, Jiacheng Du, et al.*2025 · 19 citations*](https://md.research.elamur.ai/free/en/papers/chen-2025-seed-prover-1-5/) 7. [**Can Large Reasoning Models Self-Train? arXiv:2505.21444**Sheikh Shafayat, Fahim Tajwar, Ruslan Salakhutdinov, et al.*2025 · 16 citations*](https://md.research.elamur.ai/free/en/papers/shafayat-2025-large-reasoning-models-self-train/) 8. [**The Open Proof Corpus: A Large-Scale Study of LLM-Generated Mathematical Proofs arXiv:2506.21621**Jasper Dekoninck, Ivo Petrov, Kristian Minchev, et al.*2025 · 11 citations*](https://md.research.elamur.ai/free/en/papers/dekoninck-2025-open-proof-corpus-large-scale/) 9. [**100 Days After DeepSeek-R1: A Survey on Replication Studies and More Directions for Reasoning Language Models arXiv:2505.00551**Chong Zhang, Yue Deng, Xiang Lin, et al.*2025 · 9 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2025-100-days-after-deepseek/) 10. [**ReForm: Reflective Autoformalization with Prospective Bounded Sequence Optimization arXiv:2510.24592**Guoxin Chen, Jing Wu, Xinjie Chen, et al.*2025 · 8 citations*](https://md.research.elamur.ai/free/en/papers/chen-2025-reform-reflective-autoformalization-prospective-bounded/) 11. **[PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated Reasoning](https://md.research.elamur.ai/free/en/papers/hu-2026-pacore-learning-scale-test-time/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jingcheng Hu, Yinmin Zhang, Shijie Shang, et al.*2026 · 5 citations* 12. [**Ring-lite: Scalable Reasoning via C3PO-Stabilized Reinforcement Learning for LLMs arXiv:2506.14731**Ling Team, Bin Hu, Cai Chen, et al.*2025 · 5 citations*](https://md.research.elamur.ai/free/en/papers/ling-team-2025-ring-lite-scalable-reasoning/) 13. **[EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving](https://md.research.elamur.ai/free/en/papers/zhou-2026-engibench-benchmark-evaluating-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiyuan Zhou, Xinlei Wang, Yirui He, et al.*2026 · 3 citations* 14. **[SHAPE: Unifying Safety, Helpfulness and Pedagogy for Educational LLMs](https://md.research.elamur.ai/free/en/papers/zhao-2026-shape-unifying-safety-helpfulness-pedagogy/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Sihang Zhao, Kangrui Yu, Youliang Yuan, et al.*2026 · 2 citations* 15. **[Distillation Traps and Guards: A Calibration Knob for LLM Distillability](https://md.research.elamur.ai/free/en/papers/zhan-2026-distillation-traps-guards-calibration-knob/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Weixiao Zhan, Yongcheng Jing, Leszek Rutkowski, et al.*2026 · 1 citation* 16. **[Evaluating Answer Leakage Robustness of LLM Tutors against Adversarial Student Attacks](https://md.research.elamur.ai/free/en/papers/zhao-2026-evaluating-answer-leakage-robustness-llm/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jin Zhao, Marta Knežević, Tanja Käser*2026 · 1 citation* 17. **[Consistency of LLMs to Comparative Statements in Mathematical Reasoning Tasks](https://md.research.elamur.ai/free/en/papers/san-2026-consistency-llms-comparative-statements-mathematical/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Aidan W. San, Daniel Juyoung Son, Xiaodong Liu, et al.*2026* 18. **[Entropy Scheduling in Reinforcement Learning for Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-entropy-scheduling-reinforcement-learning-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xingjin Wang, Howe Tissue, Lu Wang, et al.*2026* 19. **[GR1: Reinforcement-Enhanced LLM for Geoscience Reasoning](https://md.research.elamur.ai/free/en/papers/xie-2026-gr1-reinforcement-enhanced-llm-geoscience/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yule Xie, Jiaxin Ding, Cheng Deng, et al.*2026* 20. **[RouteMoA: Dynamic Routing without Pre-Inference Boosts Efficient Mixture-of-Agents](https://md.research.elamur.ai/free/en/papers/wang-2026-routemoa-dynamic-routing-without-pre/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jize Wang, Han Wu, Zhiyuan You, et al.*2026* 21. **[Training Verifier to Assessing Complex Real-World Tool-Use Trajectories](https://md.research.elamur.ai/free/en/papers/sun-2026-training-verifier-assessing-complex-real/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Linzhuang Sun, Mingyang Chen, Hao Liang, et al.*2026* 22. **[WildSci: Advancing Scientific Reasoning from In-the-Wild Literature](https://md.research.elamur.ai/free/en/papers/liu-2026-wildsci-advancing-scientific-reasoning-wild/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Tengxiao Liu, Deepak Nathani, Zekun Li, et al.*2026* 21 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/albalak-2025-big-math-large-scale-high/) — Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models