--- title: "Who cites Execution-based Code — 39 citing papers" description: "39 papers of the Elamur Research library cite «Execution-based Code Generation using Deep Reinforcement Learning», found in their…" canonical: "https://research.elamur.ai/free/en/papers/shojaee-2023-execution-based-code-generation-using/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/shojaee-2023-execution-based-code-generation-using/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Execution-based Code Generation using Deep…](https://md.research.elamur.ai/free/en/papers/shojaee-2023-execution-based-code-generation-using/) / cited by # Who cites «Execution-based Code Generation using Deep Reinforcement Learning» 39 papers of our library cite it (of the 149,439 whose bibliographies we read) · 15 with a page here, the most cited first · [← back to «Execution-based Code Generation using Deep Reinforcement Learning»](https://md.research.elamur.ai/free/en/papers/shojaee-2023-execution-based-code-generation-using/) 1. **[A Survey on Large Language Models for Code Generation](https://md.research.elamur.ai/free/en/papers/jiang-2024-survey-large-language-models-code/) [ACM Transactions on Software Engineering and Methodology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-software-engineering-and-methodology/)**Juyong Jiang, Fan Wang, Jiasi Shen, et al.*2024 · 367 citations* 2. **[OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement](https://md.research.elamur.ai/free/en/papers/zheng-2024-opencodeinterpreter-integrating-code-generation-execution/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Tianyu Zheng, Ge Zhang, Tianhao Shen, et al.*2024 · 129 citations* 3. [**RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning arXiv:2410.02089**Jonas Gehring, Kunhao Zheng, Jade Copet, et al.*2024 · 51 citations*](https://md.research.elamur.ai/free/en/papers/gehring-2024-rlef-grounding-code-llms-execution/) 4. [**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/) 5. [**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/) 6. **[ACECODER: Acing Coder RL via Automated Test-Case Synthesis](https://md.research.elamur.ai/free/en/papers/zeng-2025-acecoder-acing-coder-rl-automated/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Huaye Zeng, Dongfu Jiang, Haozhe Wang, et al.*2025 · 32 citations* 7. **[StepCoder: Improving Code Generation with Reinforcement Learning from Compiler Feedback](https://md.research.elamur.ai/free/en/papers/dou-2024-stepcoder-improving-code-generation-reinforcement/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shihan Dou, Yan Liu, Haoxiang Jia, et al.*2024 · 32 citations* 8. [**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/) 9. **[CodeContests+: High-Quality Test Case Generation for Competitive Programming](https://md.research.elamur.ai/free/en/papers/wang-2025-codecontests-high-quality-test-case/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zihan Wang, Siyao Liu, Yang Sun, et al.*2025 · 16 citations* 10. [**Process Supervision-Guided Policy Optimization for Code Generation arXiv:2410.17621**Ning Dai, Zheng Wu, Renjie Zheng, et al.*2024 · 12 citations*](https://md.research.elamur.ai/free/en/papers/dai-2024-process-supervision-guided-policy-optimization/) 11. [**Search, Verify and Feedback: Towards Next Generation Post-training Paradigm of Foundation Models via Verifier Engineering arXiv:2411.11504**Xinyan Guan, Yanjiang Liu, Xinyu Lu, et al.*2024 · 9 citations*](https://md.research.elamur.ai/free/en/papers/guan-2024-search-verify-feedback-towards-next/) 12. **[Bootstrapping Code Translation with Weighted Multilanguage Exploration](https://md.research.elamur.ai/free/en/papers/wu-2026-bootstrapping-code-translation-weighted-multilanguage/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuhan Wu, Huan Zhang, Wei Cheng, et al.*2026* 13. **[CodeRM-NT: Reward Model for Code RL without Unit Tests](https://md.research.elamur.ai/free/en/papers/xia-2026-coderm-nt-reward-model-code/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiao Xia, Dan Zhang, Tianrui Sun*2026* 14. **[SynthFix: Adaptive Neuro-Symbolic Code Vulnerability Repair](https://md.research.elamur.ai/free/en/papers/zhang-2026-synthfix-adaptive-neuro-symbolic-code/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yifan Zhang, Jieyu Li, Kexin Pei, et al.*2026* 15. **[Think Before you Write: QA-Guided Reasoning for Character Descriptions in Books](https://md.research.elamur.ai/free/en/papers/papoudakis-2026-think-before-you-write-qa/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Argyrios Papoudakis, Mirella Lapata, Frank Keller*2026* 24 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/shojaee-2023-execution-based-code-generation-using/) — Execution-based Code Generation using Deep Reinforcement Learning