---
title: "Who cites Ramana Kumar — 370 papers of our library"
description: "370 papers of the Elamur Research library cite the works of Ramana Kumar, found in their bibliographies · page 1 of 2 · 1–100; the most cited first."
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---
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# Who cites Ramana Kumar

371 citations of their works from 370 papers of our library (of the 149,439 whose bibliographies we read) · 164 with a page here, the most cited first · page 1 of 2 · 1–100 · [← Ramana Kumar](https://md.research.elamur.ai/free/en/authors/ramana-kumar/)

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1. [**The Llama 3 Herd of Models arXiv:2407.21783**Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, et al.*2024 · 7,682 citations*](https://md.research.elamur.ai/free/en/papers/grattafiori-2024-llama-3-herd-models/)
2. **[DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2025-deepseek-r1-incentivizing-reasoning-capability/) [Nature](https://md.research.elamur.ai/free/en/venues/nature/)**DeepSeek-AI, Daya Guo, Dejian Yang, et al.*2025 · 4,728 citations*
3. **[A Survey of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/) [Frontiers of Computer Science](https://md.research.elamur.ai/free/en/venues/frontiers-of-computer-science/)**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,875 citations*
4. [**Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context arXiv:2403.05530**Gemini Team, Petko Georgiev, Ving Ian Lei, et al.*2024 · 1,589 citations*](https://md.research.elamur.ai/free/en/papers/gemini-team-2024-gemini-1-5-unlocking-multimodal/)
5. [**Let's Verify Step by Step arXiv:2305.20050**Hunter Lightman, Vineet Kosaraju, Yura Burda, et al.*2023 · 1,412 citations*](https://md.research.elamur.ai/free/en/papers/lightman-2023-let-s-verify-step-step/)
6. [**OpenAI o1 System Card arXiv:2412.16720**OpenAI, Aaron Jaech, Adam Kalai, et al.*2024 · 1,110 citations*](https://md.research.elamur.ai/free/en/papers/openai-2024-openai-o1-system-card/)
7. [**Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities arXiv:2507.06261**Gheorghe Comanici, Eric Bieber, Mike Schaekermann, et al.*2025 · 974 citations*](https://md.research.elamur.ai/free/en/papers/comanici-2025-gemini-2-5-pushing-frontier/)
8. **[Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https://md.research.elamur.ai/free/en/papers/yang-2023-harnessing-power-llms-practice-survey/) [ACM Transactions on Knowledge Discovery from Data](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-knowledge-discovery-from-data/)**Jingfeng Yang, Hongye Jin, Ruixiang Tang, et al.*2023 · 814 citations*
9. **[Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting](https://md.research.elamur.ai/free/en/papers/turpin-2023-language-models-don-t-always/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Miles Turpin, Julian Michael, Ethan Perez, et al.*2023 · 586 citations*
10. [**Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters arXiv:2408.03314**Charlie Snell, Jaehoon Lee, Kelvin Xu, et al.*2024 · 567 citations*](https://md.research.elamur.ai/free/en/papers/snell-2024-scaling-llm-test-time-compute/)
11. [**Unleashing the potential of prompt engineering for large language models arXiv:2310.14735 · Patterns 6(6) 101,260 (2025)**Banghao Chen, Zhaofeng Zhang, Nicolas Langrene, et al.*2023 · 536 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-unleashing-potential-prompt-engineering-large/)
12. **[Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations](https://md.research.elamur.ai/free/en/papers/wang-2024-math-shepherd-verify-reinforce-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Peiyi Wang, Lei Li, Zhihong Shao, et al.*2024 · 497 citations*
13. **[AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback](https://md.research.elamur.ai/free/en/papers/dubois-2023-alpacafarm-simulation-framework-methods-that/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yann Dubois, Xuechen Li, Rohan Taori, et al.*2023 · 456 citations*
14. [**WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct arXiv:2308.09583**Haipeng Luo, Qingfeng Sun, Can Xu, et al.*2023 · 372 citations*](https://md.research.elamur.ai/free/en/papers/luo-2023-wizardmath-empowering-mathematical-reasoning-large/)
15. [**Challenges and Applications of Large Language Models arXiv:2307.10169**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 301 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/)
16. [**Llemma: An Open Language Model For Mathematics arXiv:2310.10631**Zhangir Azerbayev, Hailey Schoelkopf, Keiran Paster, et al.*2023 · 286 citations*](https://md.research.elamur.ai/free/en/papers/azerbayev-2023-llemma-open-language-model-mathematics/)
17. [**Scaling Relationship on Learning Mathematical Reasoning with Large Language Models arXiv:2308.01825**Zheng Yuan, Hongyi Yuan, Chengpeng Li, et al.*2023 · 261 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2023-scaling-relationship-learning-mathematical-reasoning/)
18. **[Humanity's Last Exam](https://md.research.elamur.ai/free/en/papers/phan-2025-humanity-s-last-exam/) [Nature](https://md.research.elamur.ai/free/en/venues/nature/)**Long Phan, Alice Gatti, Ziwen Han, et al.*2025 · 256 citations*
19. [**Reinforced Self-Training (ReST) for Language Modeling arXiv:2308.08998**Caglar Gulcehre, Tom Le Paine, Srivatsan Srinivasan, et al.*2023 · 224 citations*](https://md.research.elamur.ai/free/en/papers/gulcehre-2023-reinforced-self-training-rest-language/)
20. [**Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment arXiv:2308.05374**Yang Liu; Yuanshun Yao; Jean-Francois Ton; Xiaoying Zhang; Ruocheng Guo; Hao Cheng; Yegor Klochkov; Muhammad Faaiz Taufiq;, Hang Li*2023 · 223 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-trustworthy-llms-survey-guideline-evaluating/)
21. [**Autoformalization with Large Language Models arXiv:2205.12615**Yuhuai Wu, Albert Q. Jiang, Wenda Li, et al.*2022 · 204 citations*](https://md.research.elamur.ai/free/en/papers/wu-2022-autoformalization-large-language-models/)
22. **[Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-towards-reasoning-era-survey-long/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Libo Qin, Jinhao Liu, et al.*2025 · 176 citations*
23. [**Baldur: Whole-Proof Generation and Repair with Large Language Models arXiv:2303.04910**Emily First, Markus N. Rabe, Talia Ringer, et al.*2023 · 175 citations*](https://md.research.elamur.ai/free/en/papers/first-2023-baldur-whole-proof-generation-repair/)
24. [**Improve Mathematical Reasoning in Language Models by Automated Process Supervision arXiv:2406.06592**Liangchen Luo, Yinxiao Liu, Rosanne Liu, et al.*2024 · 168 citations*](https://md.research.elamur.ai/free/en/papers/luo-2024-improve-mathematical-reasoning-language-models/)
25. **[The Lessons of Developing Process Reward Models in Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/zhang-2025-lessons-developing-process-reward-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhenru Zhang, Chujie Zheng, Yangzhen Wu, et al.*2025 · 161 citations*
26. **[When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs](https://md.research.elamur.ai/free/en/papers/kamoi-2024-when-can-llms-actually-correct/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Ryo Kamoi, Yusen Zhang, Nan Zhang, et al.*2024 · 161 citations*
27. [**Training Language Models to Self-Correct via Reinforcement Learning arXiv:2409.12917**Aviral Kumar, Vincent Zhuang, Rishabh Agarwal, et al.*2024 · 159 citations*](https://md.research.elamur.ai/free/en/papers/kumar-2024-training-language-models-self-correct/)
28. [**Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models arXiv:2503.16419**Yang Sui, Yu-Neng Chuang, Guanchu Wang, et al.*2025 · 157 citations*](https://md.research.elamur.ai/free/en/papers/sui-2025-stop-overthinking-survey-efficient-reasoning/)
29. [**Generative Verifiers: Reward Modeling as Next-Token Prediction arXiv:2408.15240**Lunjun Zhang, Arian Hosseini, Hritik Bansal, et al.*2024 · 152 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-generative-verifiers-reward-modeling-as/)
30. [**Process Reinforcement through Implicit Rewards arXiv:2502.01456**Ganqu Cui, Lifan Yuan, Zefan Wang, et al.*2025 · 151 citations*](https://md.research.elamur.ai/free/en/papers/cui-2025-process-reinforcement-through-implicit-rewards/)
31. **[Self-Evaluation Guided Beam Search for Reasoning](https://md.research.elamur.ai/free/en/papers/xie-2023-self-evaluation-guided-beam-search/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yuxi Xie, Kenji Kawaguchi, Yiran Zhao, et al.*2023 · 149 citations*
32. **[Automatically Correcting Large Language Models: Surveying the Landscape of Diverse Automated Correction Strategies](https://md.research.elamur.ai/free/en/papers/pan-2024-automatically-correcting-large-language-models/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Liangming Pan, Michael Saxon, Wenda Xu, et al.*2024 · 140 citations*
33. **[ReFT: Reasoning with Reinforced Fine-Tuning](https://md.research.elamur.ai/free/en/papers/trung-2024-reft-reasoning-reinforced-fine-tuning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Luong Trung, Xinbo Zhang, Zhanming Jie, et al.*2024 · 134 citations*
34. [**The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning arXiv:2403.03218**Nathaniel Li, Alexander Pan, Anjali Gopal, et al.*2024 · 127 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-wmdp-benchmark-measuring-reducing-malicious/)
35. [**Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future arXiv:2309.15402**Zheng Chu, Jingchang Chen, Qianglong Chen, et al.*2023 · 125 citations*](https://md.research.elamur.ai/free/en/papers/chu-2023-navigate-through-enigmatic-labyrinth-survey/)
36. [**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/)
37. [**Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models arXiv:2408.00724**Yangzhen Wu, Zhiqing Sun, Shanda Li, et al.*2024 · 122 citations*](https://md.research.elamur.ai/free/en/papers/wu-2024-inference-scaling-laws-empirical-analysis/)
38. [**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/)
39. **[Deliberative Alignment: Reasoning Enables Safer Language Models](https://md.research.elamur.ai/free/en/papers/guan-2024-deliberative-alignment-reasoning-enables-safer/) [SuperIntelligence - Robotics - Safety & Alignment](https://md.research.elamur.ai/free/en/venues/superintelligence-robotics-safety-alignment/)**Melody Y. Guan, Manas Joglekar, Eric Wallace, et al.*2024 · 115 citations*
40. [**V-STaR: Training Verifiers for Self-Taught Reasoners arXiv:2402.06457**Arian Hosseini, Xingdi Yuan, Nikolay Malkin, et al.*2024 · 114 citations*](https://md.research.elamur.ai/free/en/papers/hosseini-2024-v-star-training-verifiers-self/)
41. [**Can LLM-Generated Misinformation Be Detected? arXiv:2309.13788**Canyu Chen, Kai Shu*2023 · 108 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-llm-generated-misinformation-detected/)
42. [**JudgeBench: A Benchmark for Evaluating LLM-based Judges arXiv:2410.12784**Sijun Tan, Siyuan Zhuang, Kyle Montgomery, et al.*2024 · 101 citations*](https://md.research.elamur.ai/free/en/papers/tan-2024-judgebench-benchmark-evaluating-llm-based/)
43. [**Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning arXiv:2410.08146**Amrith Setlur, Chirag Nagpal, Adam Fisch, et al.*2024 · 93 citations*](https://md.research.elamur.ai/free/en/papers/setlur-2024-rewarding-progress-scaling-automated-process/)
44. [**Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models arXiv:2503.06749**Wenxuan Huang, Bohan Jia, Zijie Zhai, et al.*2025 · 92 citations*](https://md.research.elamur.ai/free/en/papers/huang-2025-vision-r1-incentivizing-reasoning-capability/)
45. [**InternLM-Math: Open Math Large Language Models Toward Verifiable Reasoning arXiv:2402.06332**Huaiyuan Ying, Shuo Zhang, Linyang Li, et al.*2024 · 90 citations*](https://md.research.elamur.ai/free/en/papers/ying-2024-internlm-math-open-math-large/)
46. [**Learning to Prove Theorems via Interacting with Proof Assistants arXiv:1905.09381**Kaiyu Yang, Jia Deng*2019 · 90 citations*](https://md.research.elamur.ai/free/en/papers/yang-2019-learning-prove-theorems-interacting-proof/)
47. [**Foundational Challenges in Assuring Alignment and Safety of Large Language Models arXiv:2404.09932**Usman Anwar, Abulhair Saparov, Javier Rando, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/anwar-2024-foundational-challenges-assuring-alignment-safety/)
48. [**O1 Replication Journey: A Strategic Progress Report — Part 1 arXiv:2410.18982**Yiwei Qin, Xuefeng Li, Haoyang Zou, et al.*2024 · 86 citations*](https://md.research.elamur.ai/free/en/papers/qin-2024-o1-replication-journey-strategic-progress/)
49. **[Evaluating Mathematical Reasoning Beyond Accuracy](https://md.research.elamur.ai/free/en/papers/xia-2024-evaluating-mathematical-reasoning-accuracy/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Shijie Xia, Xuefeng Li, Yixin Liu, et al.*2024 · 85 citations*
50. [**Proof Artifact Co-training for Theorem Proving with Language Models arXiv:2102.06203**Jesse Michael Han, Jason Rute, Yuhuai Wu, et al.*2021 · 82 citations*](https://md.research.elamur.ai/free/en/papers/han-2021-proof-artifact-co-training-theorem/)
51. **[Interpretability at Scale: Identifying Causal Mechanisms in Alpaca](https://md.research.elamur.ai/free/en/papers/wu-2023-interpretability-scale-identifying-causal-mechanisms/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhengxuan Wu, Atticus Geiger, Thomas Icard, et al.*2023 · 81 citations*
52. **[Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing](https://md.research.elamur.ai/free/en/papers/tian-2024-toward-self-improvement-llms-imagination/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Ye Tian, Baolin Peng, Linfeng Song, et al.*2024 · 80 citations*
53. [**A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well? arXiv:2503.24235**Qiyuan Zhang, Fuyuan Lyu, Zexu Sun, et al.*2025 · 79 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2025-survey-test-time-scaling-large/)
54. **[OVM, Outcome-supervised Value Models for Planning in Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/yu-2023-ovm-outcome-supervised-value-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Fei Yu, Anningzhe Gao, Benyou Wang*2023 · 78 citations*
55. **[Recursive Introspection: Teaching Language Model Agents How to Self-Improve](https://md.research.elamur.ai/free/en/papers/qu-2024-recursive-introspection-teaching-language-model/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yuxiao Qu, Tianjun Zhang, Naman Garg, et al.*2024 · 75 citations*
56. [**Cumulative Reasoning with Large Language Models arXiv:2308.04371**Yifan Zhang, Jingqin Yang, Yang Yuan, et al.*2023 · 74 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-cumulative-reasoning-large-language-models/)
57. [**Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents arXiv:2408.07199**Pranav Putta, Edmund Mills, Naman Garg, et al.*2024 · 68 citations*](https://md.research.elamur.ai/free/en/papers/putta-2024-agent-q-advanced-reasoning-learning/)
58. [**Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models arXiv:2311.08692**Keming Lu, Hongyi Yuan, Runji Lin, et al.*2023 · 66 citations*](https://md.research.elamur.ai/free/en/papers/lu-2023-routing-expert-efficient-reward-guided/)
59. [**Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation arXiv:2503.11926**Bowen Baker, Joost Huizinga, Leo Gao, et al.*2025 · 63 citations*](https://md.research.elamur.ai/free/en/papers/baker-2025-monitoring-reasoning-models-misbehavior-risks/)
60. **[PRMBench: A Fine-grained and Challenging Benchmark for Process-Level Reward Models](https://md.research.elamur.ai/free/en/papers/song-2025-prmbench-fine-grained-challenging-benchmark/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mingyang Song, Zhaochen Su, Xiaoye Qu, et al.*2025 · 63 citations*
61. [**VinePPO: Refining Credit Assignment in RL Training of LLMs arXiv:2410.01679**Amirhossein Kazemnejad, Milad Aghajohari, Eva Portelance, et al.*2024 · 63 citations*](https://md.research.elamur.ai/free/en/papers/kazemnejad-2024-vineppo-refining-credit-assignment-rl/)
62. [**Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes Correct Reasoning in Base LLMs arXiv:2506.14245**Xumeng Wen, Zihan Liu, Shun Zheng, et al.*2025 · 62 citations*](https://md.research.elamur.ai/free/en/papers/wen-2025-reinforcement-learning-verifiable-rewards-implicitly/)
63. [**Teaching Large Language Models to Reason with Reinforcement Learning arXiv:2403.04642**Alex Havrilla, Yuqing Du, Sharath Chandra Raparthy, et al.*2024 · 61 citations*](https://md.research.elamur.ai/free/en/papers/havrilla-2024-teaching-large-language-models-reason/)
64. [**Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in Chinchilla arXiv:2307.09458**Tom Lieberum, Matthew Rahtz, Janos Kramar, et al.*2023 · 58 citations*](https://md.research.elamur.ai/free/en/papers/lieberum-2023-circuit-analysis-interpretability-scale-evidence/)
65. [**Teaching Arithmetic to Small Transformers arXiv:2307.03381**Nayoung Lee, Kartik Sreenivasan, Jason D. Lee, et al.*2023 · 57 citations*](https://md.research.elamur.ai/free/en/papers/lee-2023-teaching-arithmetic-small-transformers/)
66. **[OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI](https://md.research.elamur.ai/free/en/papers/huang-2024-olympicarena-benchmarking-multi-discipline-cognitive/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhen Huang, Zengzhi Wang, Shijie Xia, et al.*2024 · 56 citations*
67. **[Parsel: Algorithmic Reasoning with Language Models by Composing Decompositions](https://md.research.elamur.ai/free/en/papers/zelikman-2023-parsel-algorithmic-reasoning-language-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Eric Zelikman, Qian Huang, Gabriel Poesia, et al.*2023 · 56 citations*
68. [**Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning arXiv:2406.14283**Chaojie Wang, Yanchen Deng, Zhiyi Lyu, et al.*2024 · 53 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-q-improving-multi-step-reasoning/)
69. **[Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision](https://md.research.elamur.ai/free/en/papers/sun-2024-easy-hard-generalization-scalable-alignment/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhiqing Sun, Longhui Yu, Yikang Shen, et al.*2024 · 51 citations*
70. [**From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models arXiv:2406.16838**Sean Welleck, Amanda Bertsch, Matthew Finlayson, et al.*2024 · 51 citations*](https://md.research.elamur.ai/free/en/papers/welleck-2024-decoding-meta-generation-inference-time/)
71. [**OpenR: An Open Source Framework for Advanced Reasoning with Large Language Models arXiv:2410.09671**Jun Wang, Meng Fang, Ziyu Wan, et al.*2024 · 50 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-openr-open-source-framework-advanced/)
72. [**LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning arXiv:2410.02884**Di Zhang, Jianbo Wu, Jingdi Lei, et al.*2024 · 49 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-llama-berry-pairwise-optimization-o1/)
73. [**A Survey on Deep Learning for Theorem Proving arXiv:2404.09939**Zhaoyu Li, Jialiang Sun, Logan Murphy, et al.*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-survey-deep-learning-theorem-proving/)
74. [**ARGS: Alignment as Reward-Guided Search arXiv:2402.01694**Maxim Khanov, Jirayu Burapacheep, Yixuan Li*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/khanov-2024-args-alignment-reward-guided-search/)
75. [**Don't throw away your value model! Generating more preferable text with Value-Guided Monte-Carlo Tree Search decoding arXiv:2309.15028**Jiacheng Liu, Andrew Cohen, Ramakanth Pasunuru, et al.*2023 · 46 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-don-t-throw-away-value/)
76. [**GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements arXiv:2402.10963**Alex Havrilla, Sharath Raparthy, Christoforus Nalmpantis, et al.*2024 · 45 citations*](https://md.research.elamur.ai/free/en/papers/havrilla-2024-glore-where-improve-llm-reasoning/)
77. [**TinyGSM: achieving >80% on GSM8k with small language models arXiv:2312.09241**Bingbin Liu, Sebastien Bubeck, Ronen Eldan, et al.*2023 · 45 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-tinygsm-achieving-80-gsm8k-small/)
78. [**Entropy-Aware On-Policy Distillation of Language Models arXiv:2603.07079**Woogyeol Jin, Taywon Min, Yongjin Yang, et al.*2026 · 44 citations*](https://md.research.elamur.ai/free/en/papers/jin-2026-entropy-aware-policy-distillation-language/)
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80. **[Multi-step Problem Solving Through a Verifier: An Empirical Analysis on Model-induced Process Supervision](https://md.research.elamur.ai/free/en/papers/wang-2024-multi-step-problem-solving-through/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zihan Wang, Yunxuan Li, Yuexin Wu, et al.*2024 · 43 citations*
81. [**Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling arXiv:2502.06703**Runze Liu, Junqi Gao, Jian Zhao, et al.*2025 · 41 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-1b-llm-surpass-405b-llm/)
82. [**DynaMath: A Dynamic Visual Benchmark for Evaluating Mathematical Reasoning Robustness of Vision Language Models arXiv:2411.00836**Chengke Zou, Xingang Guo, Rui Yang, et al.*2024 · 41 citations*](https://md.research.elamur.ai/free/en/papers/zou-2024-dynamath-dynamic-visual-benchmark-evaluating/)
83. [**GPT Can Solve Mathematical Problems Without a Calculator arXiv:2309.03241**Zhen Yang, Ming Ding, Qingsong Lv, et al.*2023 · 41 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-gpt-solve-mathematical-problems-calculator/)
84. [**INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving arXiv:2007.02924**Yuhuai Wu, Albert Qiaochu Jiang, Jimmy Ba, et al.*2020 · 41 citations*](https://md.research.elamur.ai/free/en/papers/wu-2020-int-inequality-benchmark-evaluating-generalization/)
85. [**Let's reward step by step: Step-Level reward model as the Navigators for Reasoning arXiv:2310.10080**Qianli Ma, Haotian Zhou, Tingkai Liu, et al.*2023 · 40 citations*](https://md.research.elamur.ai/free/en/papers/ma-2023-let-s-reward-step-step/)
86. [**A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems arXiv:2504.09037**Zixuan Ke, Fangkai Jiao, Yifei Ming, et al.*2025 · 39 citations*](https://md.research.elamur.ai/free/en/papers/ke-2025-survey-frontiers-llm-reasoning-inference/)
87. [**LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning arXiv:2101.06223**Yuhuai Wu, Markus Rabe, Wenda Li, et al.*2021 · 39 citations*](https://md.research.elamur.ai/free/en/papers/wu-2021-lime-learning-inductive-bias-primitives/)
88. [**Introducing v0.5 of the AI Safety Benchmark from MLCommons arXiv:2404.12241**Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed, et al.*2024 · 38 citations*](https://md.research.elamur.ai/free/en/papers/vidgen-2024-introducing-v0-5-ai-safety/)
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90. [**Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn't arXiv:2503.16219**Quy-Anh Dang, Chris Ngo*2025 · 37 citations*](https://md.research.elamur.ai/free/en/papers/dang-2025-reinforcement-learning-reasoning-small-llms/)
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93. [**Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning arXiv:2402.05808**Zhiheng Xi, Wenxiang Chen, Boyang Hong, et al.*2024 · 36 citations*](https://md.research.elamur.ai/free/en/papers/xi-2024-training-large-language-models-reasoning/)
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100. [**NExT: Teaching Large Language Models to Reason about Code Execution arXiv:2404.14662**Ansong Ni, Miltiadis Allamanis, Arman Cohan, et al.*2024 · 30 citations*](https://md.research.elamur.ai/free/en/papers/ni-2024-next-teaching-large-language-models/)

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