--- title: "Who cites Language Models Can Teach — 38 citing papers" description: "38 papers of the Elamur Research library cite «Language Models Can Teach Themselves to Program Better», found in their bibliographies…" canonical: "https://research.elamur.ai/free/en/papers/haluptzok-2023-language-models-can-teach-themselves/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/haluptzok-2023-language-models-can-teach-themselves/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Language Models Can Teach Themselves to Program…](https://md.research.elamur.ai/free/en/papers/haluptzok-2023-language-models-can-teach-themselves/) / cited by # Who cites «Language Models Can Teach Themselves to Program Better» 38 papers of our library cite it (of the 149,439 whose bibliographies we read) · 18 with a page here, the most cited first · [← back to «Language Models Can Teach Themselves to Program Better»](https://md.research.elamur.ai/free/en/papers/haluptzok-2023-language-models-can-teach-themselves/) 1. [**Will we run out of data? Limits of LLM scaling based on human-generated data arXiv:2211.04325**Pablo Villalobos, Anson Ho, Jaime Sevilla, et al.*2022 · 198 citations*](https://md.research.elamur.ai/free/en/papers/villalobos-2022-will-run-out-data-limits/) 2. [**CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution arXiv:2401.03065**Alex Gu, Baptiste Rozière, Hugh Leather, et al.*2024 · 122 citations*](https://md.research.elamur.ai/free/en/papers/gu-2024-cruxeval-benchmark-code-reasoning-understanding/) 3. [**Quiet-STaR: Language models can teach themselves to think before speaking***2024 · 122 citations*](https://md.research.elamur.ai/free/en/papers/al-2024-quiet-star-language-models-teach/) 4. [**Best Practices and Lessons Learned on Synthetic Data arXiv:2404.07503**Ruibo Liu, Jerry Wei, Fangyu Liu, et al.*2024 · 119 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-best-practices-lessons-learned-synthetic/) 5. [**Agent Workflow Memory arXiv:2409.07429**Zora Zhiruo Wang, Jiayuan Mao, Daniel Fried, et al.*2024 · 86 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-agent-workflow-memory/) 6. **[Execution-Based Evaluation for Open-Domain Code Generation](https://md.research.elamur.ai/free/en/papers/wang-2023-execution-based-evaluation-open-domain/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhiruo Wang, Shuyan Zhou, Daniel Fried, et al.*2023 · 69 citations* 7. **[Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models](https://md.research.elamur.ai/free/en/papers/yang-2024-buffer-thoughts-thought-augmented-reasoning/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Ling Yang, Zhaochen Yu, Tianjun Zhang, et al.*2024 · 67 citations* 8. [**Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding arXiv:2401.12954**Mirac Suzgun, Adam Tauman Kalai*2024 · 62 citations*](https://md.research.elamur.ai/free/en/papers/suzgun-2024-meta-prompting-enhancing-language-models/) 9. **[Absolute Zero: Reinforced Self-play Reasoning with Zero Data](https://md.research.elamur.ai/free/en/papers/zhao-2025-absolute-zero-reinforced-self-play/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Andrew Zhao, Yiran Wu, Yang Yue, et al.*2025 · 57 citations* 10. [**LLM-SR: Scientific Equation Discovery via Programming with Large Language Models arXiv:2404.18400**Parshin Shojaee, Kazem Meidani, Shashank Gupta, et al.*2024 · 47 citations*](https://md.research.elamur.ai/free/en/papers/shojaee-2024-llm-sr-scientific-equation/) 11. [**STP: Self-play LLM Theorem Provers with Iterative Conjecturing and Proving arXiv:2502.00212**Kefan Dong, Tengyu Ma*2025 · 43 citations*](https://md.research.elamur.ai/free/en/papers/dong-2025-stp-self-play-llm-theorem/) 12. [**Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective arXiv:2412.14135**Zhiyuan Zeng, Qinyuan Cheng, Zhangyue Yin, et al.*2024 · 33 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2024-scaling-search-learning-roadmap-reproduce/) 13. **[SelfCodeAlign: Self-Alignment for Code Generation](https://md.research.elamur.ai/free/en/papers/wei-2024-selfcodealign-self-alignment-code-generation/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yuxiang Wei, Federico Cassano, Jiawei Liu, et al.*2024 · 32 citations* 14. **[Learning Formal Mathematics From Intrinsic Motivation](https://md.research.elamur.ai/free/en/papers/poesia-2024-learning-formal-mathematics-intrinsic-motivation/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Gabriel Poesia, David Broman, Nick Haber, et al.*2024 · 23 citations* 15. **[DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning Graph](https://md.research.elamur.ai/free/en/papers/zhang-2024-darg-dynamic-evaluation-large-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhehao Zhang, Jiaao Chen, Diyi Yang*2024 · 17 citations* 16. [**LLM-Assisted Code Cleaning For Training Accurate Code Generators arXiv:2311.14904**Naman Jain, Tianjun Zhang, Wei-Lin Chiang, et al.*2023 · 17 citations*](https://md.research.elamur.ai/free/en/papers/jain-2023-llm-assisted-code-cleaning-training/) 17. [**Autoformalizing Natural Language to First-Order Logic: A Case Study in Logical Fallacy Detection arXiv:2405.02318**Abhinav Lalwani, Tasha Kim, Lovish Chopra, et al.*2024 · 15 citations*](https://md.research.elamur.ai/free/en/papers/lalwani-2024-autoformalizing-natural-language-first-order/) 18. [**QuantAgent: Seeking Holy Grail in Trading by Self-Improving Large Language Model arXiv:2402.03755**Saizhuo Wang, Hang Yuan, Lionel M. Ni, et al.*2024 · 10 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-quantagent-seeking-holy-grail-trading/) 20 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/haluptzok-2023-language-models-can-teach-themselves/) — Language Models Can Teach Themselves to Program Better