🧑‍🔬 Who cites Hung Le

191 citations of their works from 190 papers of our library (of the 149,439 whose bibliographies we read) · 38 with a page here, the most cited first · ← Hung Le

1. Reflexion: Language Agents with Verbal Reinforcement LearningConference on Neural Information Processing Systems (NeurIPS)Noah Shinn, Federico Cassano, Edward Berman, et al.2023 · 2,271 citations
2. Code Llama: Open Foundation Models for CodearXiv:2308.12950Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, et al.2024 · 1,430 citations
3. DAPO: An Open-Source LLM Reinforcement Learning System at ScalearXiv:2503.14476Qiying Yu, Zheng Zhang, Ruofei Zhu, et al.2025 · 668 citations
4. A Survey on Large Language Models for Code GenerationarXiv:2406.00515Juyong Jiang, Fan Wang, Jiasi Shen, et al.2024 · 367 citations
5. CRITIC: Large Language Models Can Self-Correct with Tool-Interactive CritiquingarXiv:2305.11738Zhibin Gou, Zhihong Shao, Yeyun Gong, et al.2023 · 326 citations
6. Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' AlignmentarXiv:2308.05374Yang Liu; Yuanshun Yao; Jean-Francois Ton; Xiaoying Zhang; Ruocheng Guo; Hao Cheng; Yegor Klochkov; Muhammad Faaiz Taufiq;, Hang Li2023 · 223 citations
7. Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language ModelsScience China Information SciencesQiguang Chen, Libo Qin, Jinhao Liu, et al.2025 · 176 citations
8. From System 1 to System 2: A Survey of Reasoning Large Language ModelsarXiv:2502.17419Zhong-Zhi Li, Duzhen Zhang, Ming-Liang Zhang, et al.2025 · 165 citations
9. When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMsTransactions of the Association for Computational Linguistics (TACL)Ryo Kamoi, Yusen Zhang, Nan Zhang, et al.2024 · 161 citations
10. Automatically Correcting Large Language Models: Surveying the Landscape of Diverse Automated Correction StrategiesTransactions of the Association for Computational Linguistics (TACL)Liangming Pan, Michael Saxon, Wenda Xu, et al.2024 · 140 citations
11. ReFT: Reasoning with Reinforced Fine-TuningAnnual Meeting of the Association for Computational Linguistics (ACL)Luong Trung, Xinbo Zhang, Zhanming Jie, et al.2024 · 134 citations
12. CRUXEval: A Benchmark for Code Reasoning, Understanding and ExecutionarXiv:2401.03065Alex Gu, Baptiste Rozière, Hugh Leather, et al.2024 · 122 citations
13. Best Practices and Lessons Learned on Synthetic DataarXiv:2404.07503Ruibo Liu, Jerry Wei, Fangyu Liu, et al.2024 · 119 citations
14. FireAct: Toward Language Agent Fine-tuningarXiv:2310.05915Baian Chen, Chang Shu, Ehsan Shareghi, et al.2023 · 101 citations
15. InterCode: Standardizing and Benchmarking Interactive Coding with Execution FeedbackConference on Neural Information Processing Systems (NeurIPS)John Yang, Akshara Prabhakar, Karthik Narasimhan, et al.2023 · 95 citations
16. RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement LearningarXiv:2410.02089Jonas Gehring, Kunhao Zheng, Jade Copet, et al.2024 · 51 citations
17. SelfCodeAlign: Self-Alignment for Code GenerationConference on Neural Information Processing Systems (NeurIPS)Yuxiang Wei, Federico Cassano, Jiawei Liu, et al.2024 · 32 citations
18. Can Language Models Solve Olympiad Programming?arXiv:2404.10952Quan Shi, Michael Tang, Karthik Narasimhan, et al.2024 · 28 citations
19. Linguistic Generalizability of Test-Time Scaling in Mathematical ReasoningAnnual Meeting of the Association for Computational Linguistics (ACL)Guijin Son, Jiwoo Hong, Hyunwoo Ko, et al.2025 · 24 citations
20. CodeTree: Agent-guided Tree Search for Code Generation with Large Language ModelsConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Jierui Li, Hung Le, Yingbo Zhou, et al.2025 · 21 citations
21. Code to Think, Think to Code: A Survey on Code-Enhanced Reasoning and Reasoning-Driven Code Intelligence in LLMsConference on Empirical Methods in Natural Language Processing (EMNLP)Dayu Yang, Tianyang Liu, Daoan Zhang, et al.2025 · 19 citations
22. MAPoRL: Multi-Agent Post-Co-Training for Collaborative Large Language Models with Reinforcement LearningAnnual Meeting of the Association for Computational Linguistics (ACL)Chanwoo Park, Seungju Han, Xingzhi Guo, et al.2025 · 15 citations
23. Preference Optimization for Reasoning with Pseudo FeedbackarXiv:2411.16345Fangkai Jiao, Geyang Guo, Xingxing Zhang, et al.2024 · 15 citations
24. RAG-Critic: Leveraging Automated Critic-Guided Agentic Workflow for Retrieval Augmented GenerationAnnual Meeting of the Association for Computational Linguistics (ACL)Guanting Dong, Jiajie Jin, Xiaoxi Li, et al.2025 · 14 citations
25. NeuReasoner: Towards Explainable, Controllable, and Unified Reasoning via Mixture-of-NeuronsAnnual Meeting of the Association for Computational Linguistics (ACL)Haonan Dong, Kehan Jiang, Haoran Ye, et al.2026 · 13 citations
26. Problem Solving Through Human–AI Preference-based CooperationComputational LinguisticsSubhabrata Dutta, Timo Kaufmann, Goran Glavaš, et al.2025 · 9 citations
27. M2RC-EVAL: Massively Multilingual Repository-level Code Completion EvaluationAnnual Meeting of the Association for Computational Linguistics (ACL)Jiaheng Liu, Ken Deng, Congnan Liu, et al.2025 · 8 citations
28. CODERL+: Improving Code Generation via Reinforcement with Execution Semantics AlignmentAnnual Meeting of the Association for Computational Linguistics (ACL)Xue Jiang, Yihong Dong, Mengyang Liu, et al.2026 · 5 citations
29. a1: Steep Test-time Scaling Law via Environment Augmented GenerationFindings of the Association for Computational LinguisticsLingrui Mei, Shenghua Liu, Yiwei Wang, et al.2026 · 2 citations
30. One Refiner to Unlock Them All: Inference-Time Reasoning Elicitation via Reinforcement Query RefinementAnnual Meeting of the Association for Computational Linguistics (ACL)Yixiao Zhou, Dongzhou Cheng, Zhiliang wu, et al.2026 · 2 citations
31. Scaling Laws for Code: Every Programming Language MattersFindings of the Association for Computational LinguisticsJian Yang, Shuyue Guo, Linzheng Chai, et al.2026 · 2 citations
32. Scaling Test-Time Compute to Achieve IOI Gold Medal with Open-Weight ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Mehrzad Samadi, Aleksander Ficek, Sean Narenthiran, et al.2026 · 1 citation
33. SWE-Mutation: Can LLMs Generate Reliable Test Suites in Software Engineering?Findings of the Association for Computational LinguisticsYuxuan Sun, Yuze Zhao, Yufeng Wang, et al.2026 · 1 citation
34. The Bidirectional Process Reward ModelAnnual Meeting of the Association for Computational Linguistics (ACL)Lingyin Zhang, Jun Gao, Xiaoxue Ren, et al.2026 · 1 citation
35. Deep Learning Based Code Generation Methods: Literature Review (基于深度学习的代码生成方法研究进展)Journal of SoftwareZezhou Yang (杨泽洲), Sirong Chen (陈思榕), Cuiyun Gao (高翠芸), et al.2023 · 1 citation
36. Aligning Text, Code, and Vision: A Multi-Objective Reinforcement Learning Framework for Text-to-VisualizationConference of the European Chapter of the Association for Computational Linguistics (EACL)Mizanur Rahman, Mohammed Saidul Islam, Md Tahmid Rahman Laskar, et al.2026
37. CoDial: Interpretable Task-Oriented Dialogue Systems Through Dialogue Flow AlignmentAnnual Meeting of the Association for Computational Linguistics (ACL)Radin Shayanfar, Chu Fei Luo, Rohan V Bhambhoria, et al.2026
38. Survey on Code Generation with LLM-based Agents (基于大语言模型智能体的代码生成综述)Journal of SoftwareYihong Dong (董益宏), Xue Jiang (姜雪), Jiaru Qian (钱家如), et al.2025

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