---
title: "Who cites MARIO: MAth Reasoning with — 62 citing papers"
description: "62 papers of the Elamur Research library cite «MARIO: MAth Reasoning with code Interpreter Output - A Reproducible Pipeline», found in their…"
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---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [MARIO: MAth Reasoning with code Interpreter…](https://md.research.elamur.ai/free/en/papers/liao-2024-mario-math-reasoning-code-interpreter/) / cited by

# Who cites «MARIO: MAth Reasoning with code Interpreter Output - A Reproducible Pipeline»

62 papers of our library cite it (of the 149,439 whose bibliographies we read) · 24 with a page here, the most cited first · [← back to «MARIO: MAth Reasoning with code Interpreter Output - A Reproducible Pipeline»](https://md.research.elamur.ai/free/en/papers/liao-2024-mario-math-reasoning-code-interpreter/)

1. [**Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement arXiv:2409.12122**An Yang, Beichen Zhang, Binyuan Hui, et al.*2024 · 417 citations*](https://md.research.elamur.ai/free/en/papers/yang-2024-qwen2-5-math-technical-report/)
2. **[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*
3. **[The Lessons of Developing Process Reward Models in Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/zhang-2025-lessons-developing-process-reward-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhenru Zhang, Chujie Zheng, Yangzhen Wu, et al.*2025 · 161 citations*
4. [**rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking arXiv:2501.04519**Xinyu Guan, Li Lyna Zhang, Yifei Liu, et al.*2025 · 146 citations*](https://md.research.elamur.ai/free/en/papers/guan-2025-rstar-math-small-llms-can/)
5. **[AlphaMath Almost Zero: Process Supervision without Process](https://md.research.elamur.ai/free/en/papers/chen-2024-alphamath-almost-zero-process-supervision/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Guoxin Chen, Minpeng Liao, Chengxi Li, et al.*2024 · 120 citations*
6. **[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/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Shubham Toshniwal, Ivan Moshkov, Sean Narenthiran, et al.*2024 · 98 citations*
7. [**ToolRL: Reward is All Tool Learning Needs arXiv:2504.13958**Cheng Qian, Emre Can Acikgoz, Qi He, et al.*2025 · 94 citations*](https://md.research.elamur.ai/free/en/papers/qian-2025-toolrl-reward-all-tool-learning/)
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. [**ToRL: Scaling Tool-Integrated RL arXiv:2503.23383**Xuefeng Li, Haoyang Zou, Pengfei Liu*2025 · 61 citations*](https://md.research.elamur.ai/free/en/papers/li-2025-torl-scaling-tool-integrated-rl/)
10. **[S²R: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/ma-2025-s-2-r-teaching-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ruotian Ma, Peisong Wang, Cheng Liu, et al.*2025 · 26 citations*
11. **[START: Self-taught Reasoner with Tools](https://md.research.elamur.ai/free/en/papers/li-2025-start-self-taught-reasoner-tools/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Chengpeng Li, Mingfeng Xue, Zhenru Zhang, et al.*2025 · 21 citations*
12. **[Code to Think, Think to Code: A Survey on Code-Enhanced Reasoning and Reasoning-Driven Code Intelligence in LLMs](https://md.research.elamur.ai/free/en/papers/yang-2025-code-think-think-code-survey/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Dayu Yang, Tianyang Liu, Daoan Zhang, et al.*2025 · 19 citations*
13. **[Efficient Reasoning for LLMs through Speculative Chain-of-Thought](https://md.research.elamur.ai/free/en/papers/wang-2026-efficient-reasoning-llms-through-speculative/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jikai Wang, Juntao Li, Jianye Hou, et al.*2026 · 6 citations*
14. **[Dynamic Sampling that Adapts: Self-Aware Iterative Data Persistent Optimization for Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/rao-2026-dynamic-sampling-that-adapts-self/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jun Rao, Xuebo Liu, Hexuan Deng, et al.*2026 · 4 citations*
15. **[Beyond Templates: Dynamic Adaptation of Reasoning Demonstrations via Feasibility-Aware Exploration](https://md.research.elamur.ai/free/en/papers/wu-2026-beyond-templates-dynamic-adaptation-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yong Wu, Weihang Pan, Ke Li, et al.*2026 · 3 citations*
16. **[MathAgent: Adversarial Evolution of Constraint Graphs for Mathematical Reasoning Data Synthesis](https://md.research.elamur.ai/free/en/papers/yu-2026-mathagent-adversarial-evolution-constraint-graphs/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zixiong Yu, Jun Rao, Guhan Chen, et al.*2026 · 3 citations*
17. **[a1: Steep Test-time Scaling Law via Environment Augmented Generation](https://md.research.elamur.ai/free/en/papers/mei-2026-a1-steep-test-time-scaling/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Lingrui Mei, Shenghua Liu, Yiwei Wang, et al.*2026 · 2 citations*
18. **[Rectified Sparse Attention for Efficient Long-Sequence Generation](https://md.research.elamur.ai/free/en/papers/sun-2026-rectified-sparse-attention-efficient-long/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yutao Sun, Tianzhu Ye, Li Dong, et al.*2026 · 1 citation*
19. **[AgentV-RL: Scaling Reward Modeling with Agentic Verifier](https://md.research.elamur.ai/free/en/papers/zhang-2026-agentv-rl-scaling-reward-modeling/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jiazheng Zhang, Ziche Fu, Zhiheng Xi, et al.*2026*
20. **[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*
21. **[From Tokens to Steps: Verification-Aware Speculative Decoding for Efficient Multi-Step Reasoning](https://md.research.elamur.ai/free/en/papers/purohit-2026-tokens-steps-verification-aware-speculative/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kiran Purohit, Ramasuri Narayanam, Soumyabrata Pal*2026*
22. **[Learning from Cognition: Enhancing RL Efficiency for LLM Reasoning via Hierarchical Metacognitive Decomposition and Refinement](https://md.research.elamur.ai/free/en/papers/sun-2026-learning-cognition-enhancing-rl-efficiency/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zexu Sun, Yongcheng Zeng, Erxue Min, et al.*2026*
23. **[NeedleChain: Measuring Intact Context Comprehension Capability of Large Language Models](https://md.research.elamur.ai/free/en/papers/moon-2026-needlechain-measuring-intact-context-comprehension/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hyeonseok Moon, Heuiseok Lim*2026*
24. **[When to Trust Tools? Adaptive Tool Trust Calibration For Tool-Integrated Math Reasoning](https://md.research.elamur.ai/free/en/papers/xu-2026-when-trust-tools-adaptive-tool/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ruotao Xu, Yixin Ji, Yu Luo, et al.*2026*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/liao-2024-mario-math-reasoning-code-interpreter/) — MARIO: MAth Reasoning with code Interpreter Output - A Reproducible Pipeline