🧑‍🔬 Who cites Nikolay Malkin

121 citations of their works from 121 papers of our library (of the 149,439 whose bibliographies we read) · 56 with a page here, the most cited first · ← Nikolay Malkin

1. Large Language Monkeys: Scaling Inference Compute with Repeated SamplingarXiv:2407.21787Bradley Brown, Jordan Juravsky, Ryan Ehrlich, et al.2024 · 270 citations
2. 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
3. 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
4. SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-trainingarXiv:2501.17161Tianzhe Chu, Yuexiang Zhai, Jihan Yang, et al.2025 · 160 citations
5. Iterative Reasoning Preference OptimizationConference on Neural Information Processing Systems (NeurIPS)Richard Yuanzhe Pang, Weizhe Yuan, Kyunghyun Cho, et al.2024 · 153 citations
6. Generative Verifiers: Reward Modeling as Next-Token PredictionarXiv:2408.15240Lunjun Zhang, Arian Hosseini, Hritik Bansal, et al.2024 · 152 citations
7. Many-Shot In-Context LearningConference on Neural Information Processing Systems (NeurIPS)Rishabh Agarwal, Avi Singh, Lei M. Zhang, et al.2024 · 142 citations
8. Quiet-STaR: Language models can teach themselves to think before speaking2024 · 122 citations
9. Rewarding Progress: Scaling Automated Process Verifiers for LLM ReasoningarXiv:2410.08146Amrith Setlur, Chirag Nagpal, Adam Fisch, et al.2024 · 93 citations
10. DART-Math: Difficulty-Aware Rejection Tuning for Mathematical Problem-SolvingarXiv:2407.13690Yuxuan Tong, Xiwen Zhang, Rui Wang, et al.2024 · 81 citations
11. A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?arXiv:2503.24235Qiyuan Zhang, Fuyuan Lyu, Zexu Sun, et al.2025 · 79 citations
12. Recursive Introspection: Teaching Language Model Agents How to Self-ImproveConference on Neural Information Processing Systems (NeurIPS)Yuxiao Qu, Tianjun Zhang, Naman Garg, et al.2024 · 75 citations
13. Cumulative Reasoning with Large Language ModelsarXiv:2308.04371Yifan Zhang, Jingqin Yang, Yang Yuan, et al.2023 · 74 citations
14. From r to Q*: Your Language Model is Secretly a Q-FunctionarXiv:2404.12358Rafael Rafailov, Joey Hejna, Ryan Park, et al.2024 · 72 citations
15. ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement LearningarXiv:2504.01296Bairu Hou, Yang Zhang, Jiabao Ji, et al.2025 · 67 citations
16. Free Process Rewards without Process LabelsarXiv:2412.01981Lifan Yuan, Wendi Li, Huayu Chen, et al.2024 · 58 citations
17. Generative Reward ModelsarXiv:2410.12832Dakota Mahan, Duy Van Phung, Rafael Rafailov, et al.2024 · 58 citations
18. RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-FoldConference on Neural Information Processing Systems (NeurIPS)Amrith Setlur, Saurabh Garg, Xinyang Geng, et al.2024 · 56 citations
19. Formal Mathematical Reasoning: A New Frontier in AIarXiv:2412.16075Kaiyu Yang, Gabriel Poesia, Jingxuan He, et al.2024 · 49 citations
20. A Survey on Deep Learning for Theorem ProvingarXiv:2404.09939Zhaoyu Li, Jialiang Sun, Logan Murphy, et al.2024 · 46 citations
21. STaR-GATE: Teaching Language Models to Ask Clarifying QuestionsarXiv:2403.19154Chinmaya Andukuri, Jan-Philipp Fränken, Tobias Gerstenberg, et al.2024 · 43 citations
22. Improve Vision Language Model Chain-of-thought ReasoningAnnual Meeting of the Association for Computational Linguistics (ACL)Ruohong Zhang, Bowen Zhang, Yanghao Li, et al.2025 · 42 citations
23. Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time ScalingarXiv:2502.06703Runze Liu, Junqi Gao, Jian Zhao, et al.2025 · 41 citations
24. Lean Copilot: Large Language Models as Copilots for Theorem Proving in LeanarXiv:2404.12534Peiyang Song, Kaiyu Yang, Anima Anandkumar2024 · 39 citations
25. RedStar: Does Scaling Long-CoT Data Unlock Better Slow-Reasoning Systems?arXiv:2501.11284Haotian Xu, Xing Wu, Weinong Wang, et al.2025 · 37 citations
26. A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super IntelligencearXiv:2507.21046Huan-ang Gao, Jiayi Geng, Wenyue Hua, et al.2025 · 36 citations
27. Vision-Language Models Can Self-Improve Reasoning via ReflectionConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Kanzhi Cheng, Li YanTao, Fangzhi Xu, et al.2025 · 35 citations
28. OpenCodeReasoning: Advancing Data Distillation for Competitive CodingarXiv:2504.01943Wasi Uddin Ahmad, Sean Narenthiran, Somshubra Majumdar, et al.2025 · 33 citations
29. AgentGym: Evolving Large Language Model-based Agents across Diverse EnvironmentsarXiv:2406.04151Zhiheng Xi, Yiwen Ding, Wenxiang Chen, et al.2024 · 33 citations
30. Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning PerspectivearXiv:2412.14135Zhiyuan Zeng, Qinyuan Cheng, Zhangyue Yin, et al.2024 · 33 citations
31. Direct Preference Optimization of Video Large Multimodal Models from Language Model RewardConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Ruohong Zhang, Liangke Gui, Zhiqing Sun, et al.2025 · 32 citations
32. Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal SamplingarXiv:2408.16737Hritik Bansal, Arian Hosseini, Rishabh Agarwal, et al.2024 · 27 citations
33. A Survey on Large Language Models for Mathematical ReasoningACM Computing SurveysPeng-Yuan Wang, Tian-Shuo Liu, Chenyang Wang, et al.2025 · 26 citations
34. Scaling Test-Time Compute Without Verification or RL is SuboptimalarXiv:2502.12118Amrith Setlur, Nived Rajaraman, Sergey Levine, et al.2025 · 26 citations
35. How Is LLM Reasoning Distracted by Irrelevant Context? An Analysis Using a Controlled BenchmarkMinglai Yang, Ethan Huang, Liang Zhang, et al.2025 · 25 citations
36. Learning Formal Mathematics From Intrinsic MotivationConference on Neural Information Processing Systems (NeurIPS)Gabriel Poesia, David Broman, Nick Haber, et al.2024 · 23 citations
37. The Limits of Inference Scaling Through ResamplingarXiv:2411.17501Benedikt Stroebl, Sayash Kapoor, Arvind Narayanan2024 · 21 citations
38. From AI for Science to Agentic Science: A Survey on Autonomous Scientific DiscoveryarXiv:2508.14111Jiaqi Wei, Yuejin Yang, Xiang Zhang, et al.2025 · 19 citations
39. Can Large Reasoning Models Self-Train?arXiv:2505.21444Sheikh Shafayat, Fahim Tajwar, Ruslan Salakhutdinov, et al.2025 · 16 citations
40. CompassVerifier: A Unified and Robust Verifier for LLMs Evaluation and Outcome RewardConference on Empirical Methods in Natural Language Processing (EMNLP)Shudong Liu, Hongwei Liu, Junnan Liu, et al.2025 · 16 citations
41. Mastering Board Games by External and Internal Planning with Language ModelsarXiv:2412.12119John Schultz, Jakub Adamek, Matej Jusup, et al.2024 · 14 citations
42. Dynamic Parallel Tree Search for Efficient LLM ReasoningAnnual Meeting of the Association for Computational Linguistics (ACL)Yifu Ding, Wentao Jiang, Shunyu Liu, et al.2025 · 13 citations
43. When To Solve, When To Verify: Compute-Optimal Problem Solving and Generative Verification for LLM ReasoningarXiv:2504.01005Nishad Singhi, Hritik Bansal, Arian Hosseini, et al.2025 · 12 citations
44. SaRO: Enhancing LLM Safety through Reasoning-based AlignmentarXiv:2504.09420Yutao Mou, Yuxiao Luo, Shikun Zhang, et al.2025 · 11 citations
45. AlphaVerus: Bootstrapping Formally Verified Code Generation through Self-Improving Translation and TreefinementarXiv:2412.06176Pranjal Aggarwal, Bryan Parno, Sean Welleck2024 · 11 citations
46. Beyond Autoregression: Fast LLMs via Self-Distillation Through TimearXiv:2410.21035Justin Deschenaux, Caglar Gulcehre2024 · 10 citations
47. HS-STaR: Hierarchical Sampling for Self-Taught Reasoners via Difficulty Estimation and Budget ReallocationConference on Empirical Methods in Natural Language Processing (EMNLP)Feng Xiong, Hongling Xu, Yifei Wang, et al.2025 · 9 citations
48. DiMo-GUI: Advancing Test-time Scaling in GUI Grounding via Modality-Aware Visual ReasoningConference on Empirical Methods in Natural Language Processing (EMNLP)Hang Wu, Hongkai Chen, Yujun Cai, et al.2025 · 8 citations
49. Superficial Self-Improved Reasoners Benefit from Model MergingConference on Empirical Methods in Natural Language Processing (EMNLP)Xiangchi Yuan, Chunhui Zhang, Zheyuan Liu, et al.2025 · 8 citations
50. SIaM: Self-Improving Code-Assisted Mathematical Reasoning of Large Language ModelsarXiv:2408.15565Dian Yu, Baolin Peng, Ye Tian, et al.2024 · 8 citations
51. A Survey on Feedback-based Multi-step Reasoning for Large Language Models on MathematicsarXiv:2502.14333Ting-Ruen Wei, Haowei Liu, Xuyang Wu, et al.2025 · 7 citations
52. Enhancing Auto-regressive Chain-of-Thought through Loop-Aligned ReasoningConference of the European Chapter of the Association for Computational Linguistics (EACL)Qifan Yu, Zhenyu He, Sijie Li, et al.2026 · 5 citations
53. EpiCaR: Knowing What You Don't Know Matters for Better Reasoning in LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Jewon Yeom, Jaewon Sok, Seonghyeon Park, et al.2026
54. RADO: Reasoning Audit-Driven Optimization for Rigorous Reasoning in High-Stakes DomainsAnnual Meeting of the Association for Computational Linguistics (ACL)Zhijie Tan, Xu Chu, Guanyu Wang, et al.2026
55. Test-Time Scaling in Multimodal Foundation Models: A Comprehensive Survey of Generation and ReasoningFindings of the Association for Computational LinguisticsCong Wan, Ying He, Zhongzhan Huang, et al.2026
56. Thinking Twice Makes Large Language Models Safer and More HelpfulFindings of the Association for Computational LinguisticsYutao Mou, Yuxiao Luo, Shikun Zhang, et al.2026

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