--- title: "Who cites DeepMath-103K: A Large-Scale, — 77 citing papers" description: "77 papers of the Elamur Research library cite «DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical…" canonical: "https://research.elamur.ai/free/en/papers/he-2025-deepmath-103k-large-scale-challenging/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/he-2025-deepmath-103k-large-scale-challenging/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [DeepMath-103K: A Large-Scale, Challenging…](https://md.research.elamur.ai/free/en/papers/he-2025-deepmath-103k-large-scale-challenging/) / cited by # Who cites «DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning» 77 papers of our library cite it (of the 149,439 whose bibliographies we read) · 19 with a page here, the most cited first · [← back to «DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning»](https://md.research.elamur.ai/free/en/papers/he-2025-deepmath-103k-large-scale-challenging/) 1. [**OpenThoughts: Data Recipes for Reasoning Models arXiv:2506.04178**Etash Guha, Ryan Marten, Sedrick Keh, et al.*2025 · 122 citations*](https://md.research.elamur.ai/free/en/papers/guha-2025-openthoughts-data-recipes-reasoning-models/) 2. **[StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason](https://md.research.elamur.ai/free/en/papers/zhang-2026-stephint-multi-level-stepwise-hints/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kaiyi Zhang, Ang Lv, Jinpeng Li, et al.*2026 · 13 citations* 3. **[CriticLean: Critic-Guided Reinforcement Learning for Mathematical Formalization](https://md.research.elamur.ai/free/en/papers/peng-2026-criticlean-critic-guided-reinforcement-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhongyuan Peng, Yifan Yao, Kaijing Ma, et al.*2026 · 12 citations* 4. [**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/) 5. [**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/) 6. [**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/) 7. [**TL;DR: Too Long, Do Re-weighting for Efficient LLM Reasoning Compression arXiv:2506.02678**Zhong-Zhi Li, Xiao Liang, Zihao Tang, et al.*2025 · 8 citations*](https://md.research.elamur.ai/free/en/papers/li-2025-tl-dr-too-long-re/) 8. **[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* 9. [**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/) 10. **[SCALER: Synthetic Scalable Adaptive Learning Environment for Reasoning](https://md.research.elamur.ai/free/en/papers/xu-2026-scaler-synthetic-scalable-adaptive-learning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Caijun Xu, Changyi Xiao, Zhongyuan Peng, et al.*2026 · 2 citations* 11. **[Confidence-Calibrated Small-Large Language Model Collaboration for Cost-Efficient Reasoning](https://md.research.elamur.ai/free/en/papers/zhang-2026-confidence-calibrated-small-large-language/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Chuang Zhang, Zizhen Zhu, Yihao Wei, et al.*2026 · 1 citation* 12. **[Easy Samples Are All You Need: Self-Evolving LLMs via Data-Efficient Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/yu-2026-easy-samples-are-all-you/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhiyin Yu, Bo Zhang, Qibin Hou, et al.*2026 · 1 citation* 13. **[AdaMix: Adaptive Mixing for Short and Long Reasoning Adapters](https://md.research.elamur.ai/free/en/papers/luo-2026-adamix-adaptive-mixing-short-long/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hao Luo, Xiao Yan, Xinyan Li, et al.*2026* 14. **[CogBench: Benchmarking Cognitive Alignment of Large Language Models in Educational Question Answering](https://md.research.elamur.ai/free/en/papers/lu-2026-cogbench-benchmarking-cognitive-alignment-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Tong Lu, Zhichun Wang, Yuanhao Sun, et al.*2026* 15. **[CoTEvol: Self-Evolving Chain-of-Thoughts for Data Synthesis in Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/wang-2026-cotevol-self-evolving-chain-thoughts/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhuo Wang, Zhuo Zhang, Yafu Li, et al.*2026* 16. **[ETR: Entropy Trend Reward for Efficient Chain-of-Thought Reasoning](https://md.research.elamur.ai/free/en/papers/xiong-2026-etr-entropy-trend-reward-efficient/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xuan Xiong, Huan Liu, Li Gu, et al.*2026* 17. **[Multilingual Chain-of-Thought Compression via Cross-Lingual Distillation](https://md.research.elamur.ai/free/en/papers/wan-2026-multilingual-chain-thought-compression-cross/) [Workshop on Multilinguality in the Era of Large Language Models](https://md.research.elamur.ai/free/en/venues/mellm/)**Jiarui Wan, Songming Zhang, Yufeng Chen*2026* 18. **[Reliable Use of Lemmas via Eligibility Reasoning and Section-Aware Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/xu-2026-reliable-use-lemmas-eligibility-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhikun Xu, Xiaodong Yu, Ben Zhou, et al.*2026* 19. **[Social Welfare Function Leaderboard: On the Emergence of LLM Agents as the Welfare Dictator](https://md.research.elamur.ai/free/en/papers/shi-2026-social-welfare-function-leaderboard-emergence/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhengliang Shi, Ruotian Ma, Jen-tse Huang, et al.*2026* 58 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/he-2025-deepmath-103k-large-scale-challenging/) — DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning