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Who cites Hunter Lightman

2,758 citations of their works from 2,420 papers of our library (of the 149,439 whose bibliographies we read) · 745 with a page here, the most cited first · page 1 of 8 · 1–100 · ← Hunter Lightman

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  1. The Llama 3 Herd of ModelsarXiv:2407.21783Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, et al.2024 · 7,682 citations
  2. Survey of Hallucination in Natural Language GenerationACM Computing SurveysZiwei Ji, Nayeon Lee, Rita Frieske, et al.2022 · 5,974 citations
  3. DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement LearningNatureDeepSeek-AI, Daya Guo, Dejian Yang, et al.2025 · 4,728 citations
  4. Qwen3 Technical ReportarXiv:2505.09388An Yang, Anfeng Li, Baosong Yang, et al.2025 · 3,382 citations
  5. A Survey of Large Language ModelsFrontiers of Computer ScienceWayne Xin Zhao, Kun Zhou, Junyi Li, et al.2023 · 2,875 citations
  6. DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language ModelsarXiv:2402.03300Zhihong Shao, Peiyi Wang, Qihao Zhu, et al.2024 · 2,115 citations
  7. Qwen Technical ReportarXiv:2309.16609Jinze Bai, Shuai Bai, Yunfei Chu, et al.2023 · 1,669 citations
  8. The Rise and Potential of Large Language Model Based Agents: A SurveyScience China Information SciencesZhiheng Xi, Wenxiang Chen, Xin Guo, et al.2023 · 1,272 citations
  9. Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language ModelsComputational LinguisticsYue Zhang, Yafu Li, Leyang Cui, et al.2023 · 977 citations
  10. s1: Simple test-time scalingConference on Empirical Methods in Natural Language Processing (EMNLP)Niklas Muennighoff, Zitong Yang, Weijia Shi, et al.2025 · 608 citations
  11. Large Language Models Can Self-ImproveConference on Empirical Methods in Natural Language Processing (EMNLP)Jiaxin Huang, Shixiang Gu, Le Hou, et al.2023 · 575 citations
  12. Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model ParametersarXiv:2408.03314Charlie Snell, Jaehoon Lee, Kelvin Xu, et al.2024 · 567 citations
  13. MetaMath: Bootstrap Your Own Mathematical Questions for Large Language ModelsarXiv:2309.12284Longhui Yu, Weisen Jiang, Han Shi, et al.2023 · 565 citations
  14. Unleashing the potential of prompt engineering for large language modelsarXiv:2310.14735 · Patterns 6(6) 101,260 (2025)Banghao Chen, Zhaofeng Zhang, Nicolas Langrene, et al.2023 · 536 citations
  15. Instruction Tuning for Large Language Models: A SurveyACM Computing SurveysShengyu Zhang, Linfeng Dong, Xiaoya Li, et al.2023 · 520 citations
  16. Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human AnnotationsAnnual Meeting of the Association for Computational Linguistics (ACL)Peiyi Wang, Lei Li, Zhihong Shao, et al.2024 · 497 citations
  17. MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual ContextsarXiv:2310.02255Pan Lu, Hritik Bansal, Tony Xia, et al.2023 · 468 citations
  18. Kimi k1.5: Scaling Reinforcement Learning with LLMsarXiv:2501.12599Kimi Team, Angang Du, Bofei Gao, et al.2025 · 431 citations
  19. Tulu 3: Pushing Frontiers in Open Language Model Post-TrainingarXiv:2411.15124Nathan Lambert, Jacob Morrison, Valentina Pyatkin, et al.2024 · 423 citations
  20. Large Language Models Cannot Self-Correct Reasoning YetarXiv:2310.01798Jie Huang, Xinyun Chen, Swaroop Mishra, et al.2023 · 412 citations
  21. WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-InstructarXiv:2308.09583Haipeng Luo, Qingfeng Sun, Can Xu, et al.2023 · 372 citations
  22. Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement LearningarXiv:2503.09516Bowen Jin, Hansi Zeng, Zhenrui Yue, et al.2025 · 322 citations
  23. Large Language Models are Better Reasoners with Self-VerificationFindings of the Association for Computational LinguisticsYixuan Weng, Minjun Zhu, Fei Xia, et al.2023 · 304 citations
  24. InternLM2 Technical ReportarXiv:2403.17297Zheng Cai, Maosong Cao, Haojiong Chen, et al.2024 · 301 citations
  25. Llemma: An Open Language Model For MathematicsarXiv:2310.10631Zhangir Azerbayev, Hailey Schoelkopf, Keiran Paster, et al.2023 · 286 citations
  26. Large Language Models for Mathematical Reasoning: Progresses and ChallengesConference of the European Chapter of the Association for Computational Linguistics (EACL)Janice Ahn, Rishu Verma, Renze Lou, et al.2024 · 280 citations
  27. Large Language Monkeys: Scaling Inference Compute with Repeated SamplingarXiv:2407.21787Bradley Brown, Jordan Juravsky, Ryan Ehrlich, et al.2024 · 270 citations
  28. Scaling Relationship on Learning Mathematical Reasoning with Large Language ModelsarXiv:2308.01825Zheng Yuan, Hongyi Yuan, Chengpeng Li, et al.2023 · 261 citations
  29. Detecting and Preventing Hallucinations in Large Vision Language ModelsProceedings of the AAAI Conference on Artificial IntelligenceAnisha Gunjal, Jihan Yin, Erhan Bas2023 · 235 citations
  30. ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem SolvingarXiv:2309.17452Zhibin Gou, Zhihong Shao, Yeyun Gong, et al.2023 · 225 citations
  31. Accelerating scientific discovery with Co-ScientistNatureJuraj Gottweis, Wei-Hung Weng, Alexander Daryin, et al.2025 · 224 citations
  32. RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human FeedbackIEEE Computer Society Conference on Computer Vision and Pattern RecognitionTianyu Yu, Yuan Yao, Haoye Zhang, et al.2023 · 217 citations
  33. Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?arXiv:2504.13837 · NeurIPS 2025 Oral; ICML 2025 AI4MATH workshop best paperYang Yue, Zhiqi Chen, Rui Lu, et al.2025 · 216 citations
  34. InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal ModelsarXiv:2504.10479Jinguo Zhu, Weiyun Wang, Zhe Chen, et al.2025 · 216 citations
  35. Search-o1: Agentic Search-Enhanced Large Reasoning ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Xiaoxi Li, Guanting Dong, Jiajie Jin, et al.2025 · 211 citations
  36. LIMO: Less is More for ReasoningarXiv:2502.03387Yixin Ye, Zhen Huang, Yang Xiao, et al.2025 · 209 citations
  37. ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree SearchConference on Neural Information Processing Systems (NeurIPS)Dan Zhang, Sining Zhoubian, Ziniu Hu, et al.2024 · 200 citations
  38. LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation MethodsarXiv:2412.05579Haitao Li, Qian Dong, Junjie Chen, et al.2024 · 195 citations
  39. Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMsarXiv:2412.21187Xingyu Chen, Jiahao Xu, Tian Liang, et al.2024 · 192 citations
  40. InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and EfficiencyarXiv:2508.18265Weiyun Wang, Zhangwei Gao, Lixin Gu, et al.2025 · 179 citations
  41. 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
  42. Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak SupervisionarXiv:2312.09390Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, et al.2023 · 172 citations
  43. Improve Mathematical Reasoning in Language Models by Automated Process SupervisionarXiv:2406.06592Liangchen Luo, Yinxiao Liu, Rosanne Liu, et al.2024 · 168 citations
  44. GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation ModelsarXiv:2508.06471Aohan Zeng, Xin Lv, Qinkai Zheng, et al.2025 · 166 citations
  45. The Lessons of Developing Process Reward Models in Mathematical ReasoningFindings of the Association for Computational LinguisticsZhenru Zhang, Chujie Zheng, Yangzhen Wu, et al.2025 · 161 citations
  46. 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
  47. SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-trainingarXiv:2501.17161Tianzhe Chu, Yuexiang Zhai, Jihan Yang, et al.2025 · 160 citations
  48. FacTool: Factuality Detection in Generative AI — A Tool Augmented Framework for Multi-Task and Multi-Domain ScenariosarXiv:2307.13528I-Chun Chern, Steffi Chern, Shiqi Chen, et al.2023 · 160 citations
  49. Training Language Models to Self-Correct via Reinforcement LearningarXiv:2409.12917Aviral Kumar, Vincent Zhuang, Rishabh Agarwal, et al.2024 · 159 citations
  50. SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the WildarXiv:2503.18892Weihao Zeng, Yuzhen Huang, Qian Liu, et al.2025 · 154 citations
  51. Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-ExpertsFindings of the Association for Computational LinguisticsHaoxiang Wang, Wei Xiong, Tengyang Xie, et al.2024 · 153 citations
  52. Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAsarXiv:2503.01743Microsoft, Abdelrahman Abouelenin, Atabak Ashfaq, et al.2025 · 152 citations
  53. Generative Verifiers: Reward Modeling as Next-Token PredictionarXiv:2408.15240Lunjun Zhang, Arian Hosseini, Hritik Bansal, et al.2024 · 152 citations
  54. Process Reinforcement through Implicit RewardsarXiv:2502.01456Ganqu Cui, Lifan Yuan, Zefan Wang, et al.2025 · 151 citations
  55. rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep ThinkingarXiv:2501.04519Xinyu Guan, Li Lyna Zhang, Yifei Liu, et al.2025 · 146 citations
  56. How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data CompositionAnnual Meeting of the Association for Computational Linguistics (ACL)Guanting Dong, Hongyi Yuan, Keming Lu, et al.2024 · 145 citations
  57. Many-Shot In-Context LearningConference on Neural Information Processing Systems (NeurIPS)Rishabh Agarwal, Avi Singh, Lei M. Zhang, et al.2024 · 142 citations
  58. R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement LearningarXiv:2503.05592Huatong Song, Jinhao Jiang, Yingqian Min, et al.2025 · 141 citations
  59. 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
  60. OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF FrameworkarXiv:2405.11143Jian Hu, Xibin Wu, Wei Shen, et al.2024 · 140 citations
  61. To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoningarXiv:2409.12183Zayne Sprague, Fangcong Yin, Juan Diego Rodriguez, et al.2024 · 138 citations
  62. 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
  63. OpenWebMath: An Open Dataset of High-Quality Mathematical Web TextarXiv:2310.06786Keiran Paster, Marco Dos Santos, Zhangir Azerbayev, et al.2023 · 132 citations
  64. Olmo 3arXiv:2512.13961Team Olmo: Allyson Ettinger, Amanda Bertsch, Bailey Kuehl, et al.2025 · 130 citations
  65. The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree SearcharXiv:2504.08066Yutaro Yamada, Robert Tjarko Lange, Cong Lu, et al.2025 · 129 citations
  66. Take a Step Back: Evoking Reasoning via Abstraction in Large Language ModelsarXiv:2310.06117Huaixiu Steven Zheng, Swaroop Mishra, Xinyun Chen, et al.2023 · 126 citations
  67. L1: Controlling How Long A Reasoning Model Thinks With Reinforcement LearningarXiv:2503.04697Pranjal Aggarwal, Sean Welleck2025 · 125 citations
  68. Mem0: Building Production-Ready AI Agents with Scalable Long-Term MemoryarXiv:2504.19413Prateek Chhikara, Dev Khant, Saket Aryan, et al.2025 · 125 citations
  69. Generative Judge for Evaluating AlignmentarXiv:2310.05470Junlong Li, Shichao Sun, Weizhe Yuan, et al.2023 · 125 citations
  70. Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and FuturearXiv:2309.15402Zheng Chu, Jingchang Chen, Qianglong Chen, et al.2023 · 125 citations
  71. Debug like a Human: A Large Language Model Debugger via Verifying Runtime Execution Step-by-steparXiv:2402.16906Li Zhong, Zilong Wang, Jingbo Shang2024 · 124 citations
  72. Large Language Model Alignment: A SurveyarXiv:2309.15025Tianhao Shen, Renren Jin, Yufei Huang, et al.2023 · 124 citations
  73. Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRsarXiv:2503.01307Kanishk Gandhi, Ayush Chakravarthy, Anikait Singh, et al.2025 · 123 citations
  74. DeepSeek-Prover-V2: Advancing Formal Mathematical Reasoning via Reinforcement Learning for Subgoal DecompositionarXiv:2504.21801Z. Z. Ren, Zhihong Shao, Junxiao Song, et al.2025 · 122 citations
  75. Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language ModelsarXiv:2408.00724Yangzhen Wu, Zhiqing Sun, Shanda Li, et al.2024 · 122 citations
  76. The Alignment Problem from a Deep Learning PerspectivearXiv:2209.00626Richard Ngo, Lawrence Chan, Soren Mindermann2022 · 121 citations
  77. Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language ModelsarXiv:2501.09686Fengli Xu, Qianyue Hao, Zefang Zong, et al.2025 · 120 citations
  78. AlphaMath Almost Zero: Process Supervision without ProcessConference on Neural Information Processing Systems (NeurIPS)Guoxin Chen, Minpeng Liao, Chengxi Li, et al.2024 · 120 citations
  79. Demystifying Long Chain-of-Thought Reasoning in LLMsarXiv:2502.03373Edward Yeo, Yuxuan Tong, Morry Niu, et al.2025 · 119 citations
  80. Best Practices and Lessons Learned on Synthetic DataarXiv:2404.07503Ruibo Liu, Jerry Wei, Fangyu Liu, et al.2024 · 119 citations
  81. Agentless: Demystifying LLM-based Software Engineering AgentsarXiv:2407.01489Chunqiu Steven Xia, Yinlin Deng, Soren Dunn, et al.2024 · 117 citations
  82. Platypus: Quick, Cheap, and Powerful Refinement of LLMsarXiv:2308.07317Ariel N. Lee, Cole J. Hunter, Nataniel Ruiz2023 · 117 citations
  83. ProcessBench: Identifying Process Errors in Mathematical ReasoningAnnual Meeting of the Association for Computational Linguistics (ACL)Chujie Zheng, Zhenru Zhang, Beichen Zhang, et al.2025 · 116 citations
  84. V-STaR: Training Verifiers for Self-Taught ReasonersarXiv:2402.06457Arian Hosseini, Xingdi Yuan, Nikolay Malkin, et al.2024 · 114 citations
  85. UI-TARS: Pioneering Automated GUI Interaction with Native AgentsarXiv:2501.12326Yujia Qin, Yining Ye, Junjie Fang, et al.2025 · 112 citations
  86. Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-VerificationarXiv:2308.07921Aojun Zhou, Ke Wang, Zimu Lu, et al.2023 · 110 citations
  87. Nemotron-4 340B Technical ReportarXiv:2406.11704Nvidia: Bo Adler, Niket Agarwal, Ashwath Aithal, et al.2024 · 107 citations
  88. Have LLMs Advanced Enough? A Challenging Problem Solving Benchmark For Large Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Daman Arora, Himanshu Singh, Mausam2023 · 107 citations
  89. Reasoning Models Don't Always Say What They ThinkarXiv:2505.05410Yanda Chen, Joe Benton, Ansh Radhakrishnan, et al.2025 · 106 citations
  90. TokenSkip: Controllable Chain-of-Thought Compression in LLMsConference on Empirical Methods in Natural Language Processing (EMNLP)Heming Xia, Chak Tou Leong, Wenjie Wang, et al.2025 · 106 citations
  91. Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement LearningarXiv:2502.14768Tian Xie, Zitian Gao, Qingnan Ren, et al.2025 · 104 citations
  92. The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem ComplexityConference on Neural Information Processing Systems (NeurIPS)Parshin Shojaee, Iman Mirzadeh, Keivan Alizadeh, et al.2025 · 104 citations
  93. The Entropy Mechanism of Reinforcement Learning for Reasoning Language ModelsarXiv:2505.22617Ganqu Cui, Yuchen Zhang, Jiacheng Chen, et al.2025 · 101 citations
  94. JudgeBench: A Benchmark for Evaluating LLM-based JudgesarXiv:2410.12784Sijun Tan, Siyuan Zhuang, Kyle Montgomery, et al.2024 · 101 citations
  95. SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step ReasoningarXiv:2308.00436Ning Miao, Yee Whye Teh, Tom Rainforth2023 · 101 citations
  96. FLASK: Fine-grained Language Model Evaluation based on Alignment Skill SetsarXiv:2307.10928Seonghyeon Ye, Doyoung Kim, Sungdong Kim, et al.2023 · 100 citations
  97. MathArena: Evaluating LLMs on Uncontaminated Math CompetitionsConference on Neural Information Processing Systems (NeurIPS)Mislav Balunović, Jasper Dekoninck, Ivo Petrov, et al.2025 · 99 citations
  98. FELM: Benchmarking Factuality Evaluation of Large Language ModelsConference on Neural Information Processing Systems (NeurIPS)Shiqi Chen, Yiran Zhao, Jinghan Zhang, et al.2023 · 99 citations
  99. Chain of Draft: Thinking Faster by Writing LessarXiv:2502.18600Silei Xu, Wenhao Xie, Lingxiao Zhao, et al.2025 · 98 citations
  100. OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning DatasetConference on Neural Information Processing Systems (NeurIPS)Shubham Toshniwal, Ivan Moshkov, Sean Narenthiran, et al.2024 · 98 citations

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