---
title: "Who cites DeepSeek-R1: Incentivizing Reasoning Capability…"
description: "3,494 papers of the Elamur Research library cite «DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning», found in their…"
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---
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# Who cites «DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning»

3,494 papers of our library cite it (of the 140,884 whose bibliographies we read) · 80 with a page here, the most cited first · [← the paper](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2025-deepseek-r1-incentivizing-reasoning-capability/)

1. [**Qwen3 Technical Report**An Yang, Anfeng Li, Baosong Yang, et al.*2025 · 3,276 citations*](https://md.research.elamur.ai/free/en/papers/yang-2025-qwen3-technical-report/)
2. [**A Survey of Large Language Models**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,847 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/)
3. [**DAPO: An Open-Source LLM Reinforcement Learning System at Scale**Qiying Yu, Zheng Zhang, Ruofei Zhu, et al.*2025 · 634 citations*](https://md.research.elamur.ai/free/en/papers/yu-2025-dapo-open-source-llm-reinforcement/)
4. [**s1: Simple test-time scaling**Niklas Muennighoff, Zitong Yang, Weijia Shi, et al.*2025 · 584 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2025-s1-simple-test-time-scaling/)
5. [**Instruction Tuning for Large Language Models: A Survey**Shengyu Zhang, Linfeng Dong, Xiaoya Li, et al.*2023 · 515 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-instruction-tuning-large-language-models/)
6. [**Large Language Models for Information Retrieval: A Survey**Yutao Zhu; Huaying Yuan; Shuting Wang; Jiongnan Liu; Wenhan Liu; Chenlong Deng; Haonan Chen; Zheng Liu; Zhicheng Dou;, Ji-Rong Wen*2023 · 335 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2023-large-language-models-information-retrieval/)
7. [**Kimi K2: Open Agentic Intelligence**Kimi Team, Yifan Bai, Yiping Bao, et al.*2025 · 331 citations*](https://md.research.elamur.ai/free/en/papers/kimi-team-2025-kimi-k2-open-agentic/)
8. [**Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning**Bowen Jin, Hansi Zeng, Zhenrui Yue, et al.*2025 · 303 citations*](https://md.research.elamur.ai/free/en/papers/jin-2025-search-r1-training-llms-reason/)
9. [**Understanding R1-Zero-Like Training: A Critical Perspective**Zichen Liu, Changyu Chen, Wenjun Li, et al.*2025 · 266 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-understanding-r1-zero-like-training/)
10. [**Search-o1: Agentic Search-Enhanced Large Reasoning Models**Xiaoxi Li, Guanting Dong, Jiajie Jin, et al.*2025 · 205 citations*](https://md.research.elamur.ai/free/en/papers/li-2025-search-o1-agentic-search-enhanced/)
11. [**LIMO: Less is More for Reasoning**Yixin Ye, Zhen Huang, Yang Xiao, et al.*2025 · 204 citations*](https://md.research.elamur.ai/free/en/papers/ye-2025-limo-less-more-reasoning/)
12. [**Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?**Yang Yue, Zhiqi Chen, Rui Lu, et al.*2025 · 196 citations*](https://md.research.elamur.ai/free/en/papers/yue-2025-reinforcement-learning-really-incentivize-reasoning/)
13. [**Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs**Xingyu Chen, Jiahao Xu, Tian Liang, et al.*2024 · 188 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-think-that-much-2-3/)
14. [**Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models**Qiguang Chen, Libo Qin, Jinhao Liu, et al.*2025 · 174 citations*](https://md.research.elamur.ai/free/en/papers/chen-2025-towards-reasoning-era-survey-long/)
15. [**A-MEM: Agentic Memory for LLM Agents**Wujiang Xu, Zujie Liang, Kai Mei, et al.*2025 · 171 citations*](https://md.research.elamur.ai/free/en/papers/xu-2025-mem-agentic-memory-llm-agents/)
16. [**Group Sequence Policy Optimization**Chujie Zheng, Shixuan Liu, Mingze Li, et al.*2025 · 166 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2025-group-sequence-policy-optimization/)
17. [**Large Language Diffusion Models**Shen Nie, Fengqi Zhu, Zebin You, et al.*2025 · 164 citations*](https://md.research.elamur.ai/free/en/papers/nie-2025-large-language-diffusion-models/)
18. [**GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models**Aohan Zeng, Xin Lv, Qinkai Zheng, et al.*2025 · 157 citations*](https://md.research.elamur.ai/free/en/papers/team-2025-4-glm-4-5-agentic/)
19. [**Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models**Yang Sui, Yu-Neng Chuang, Guanchu Wang, et al.*2025 · 153 citations*](https://md.research.elamur.ai/free/en/papers/sui-2025-stop-overthinking-survey-efficient-reasoning/)
20. [**SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild**Weihao Zeng, Yuzhen Huang, Qian Liu, et al.*2025 · 148 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2025-simplerl-zoo-investigating-taming-zero/)
21. [**Process Reinforcement through Implicit Rewards**Ganqu Cui, Lifan Yuan, Zefan Wang, et al.*2025 · 147 citations*](https://md.research.elamur.ai/free/en/papers/cui-2025-process-reinforcement-through-implicit-rewards/)
22. [**Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs**Microsoft, Abdelrahman Abouelenin, Atabak Ashfaq, et al.*2025 · 145 citations*](https://md.research.elamur.ai/free/en/papers/microsoft-2025-phi-4-mini-technical-report/)
23. [**Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning**Shenzhi Wang, Le Yu, Chang Gao, et al.*2025 · 142 citations*](https://md.research.elamur.ai/free/en/papers/wang-2025-80-20-rule-high-entropy/)
24. [**R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning**Huatong Song, Jinhao Jiang, Yingqian Min, et al.*2025 · 137 citations*](https://md.research.elamur.ai/free/en/papers/song-2025-r1-searcher-incentivizing-search/)
25. [**Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention**Jingyang Yuan, Huazuo Gao, Damai Dai, et al.*2025 · 135 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2025-native-sparse-attention-hardware/)
26. [**Token-Budget-Aware LLM Reasoning**Tingxu Han, Zhenting Wang, Chunrong Fang, et al.*2024 · 135 citations*](https://md.research.elamur.ai/free/en/papers/han-2024-token-budget-aware-llm-reasoning/)
27. [**OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework**Jian Hu, Xibin Wu, Wei Shen, et al.*2024 · 132 citations*](https://md.research.elamur.ai/free/en/papers/hu-2024-openrlhf-easy-use-scalable/)
28. [**Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model**Jingcheng Hu, Yinmin Zhang, Qi Han, et al.*2025 · 121 citations*](https://md.research.elamur.ai/free/en/papers/hu-2025-open-reasoner-zero-open-source/)
29. [**DeepSeek-Prover-V2: Advancing Formal Mathematical Reasoning via Reinforcement Learning for Subgoal Decomposition**Z. Z. Ren, Zhihong Shao, Junxiao Song, et al.*2025 · 120 citations*](https://md.research.elamur.ai/free/en/papers/ren-2025-deepseek-prover-v2-advancing-formal/)
30. [**L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning**Pranjal Aggarwal, Sean Welleck*2025 · 119 citations*](https://md.research.elamur.ai/free/en/papers/aggarwal-2025-l1-controlling-long-reasoning-model/)
31. [**DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence**DeepSeek-AI, Anyi Xu, Bangcai Lin, et al.*2026 · 117 citations*](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2026-deepseek-v4-highly-efficient/)
32. [**Demystifying Long Chain-of-Thought Reasoning in LLMs**Edward Yeo, Yuxuan Tong, Morry Niu, et al.*2025 · 113 citations*](https://md.research.elamur.ai/free/en/papers/yeo-2025-demystifying-long-chain-thought-reasoning/)
33. [**OpenThoughts: Data Recipes for Reasoning Models**Etash Guha, Ryan Marten, Sedrick Keh, et al.*2025 · 113 citations*](https://md.research.elamur.ai/free/en/papers/guha-2025-openthoughts-data-recipes-reasoning-models/)
34. [**Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs**Kanishk Gandhi, Ayush Chakravarthy, Anikait Singh, et al.*2025 · 110 citations*](https://md.research.elamur.ai/free/en/papers/gandhi-2025-cognitive-behaviors-that-enable-self/)
35. [**Olmo 3**Team Olmo: Allyson Ettinger, Amanda Bertsch, Bailey Kuehl, et al.*2025 · 109 citations*](https://md.research.elamur.ai/free/en/papers/ettinger-2025-olmo-3/)
36. [**DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments**Yuxiang Zheng, Dayuan Fu, Xiangkun Hu, et al.*2025 · 108 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2025-deepresearcher-scaling-deep-research-reinforcement/)
37. [**TokenSkip: Controllable Chain-of-Thought Compression in LLMs**Heming Xia, Chak Tou Leong, Wenjie Wang, et al.*2025 · 105 citations*](https://md.research.elamur.ai/free/en/papers/xia-2025-tokenskip-controllable-chain-thought-compression/)
38. [**Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning**Tian Xie, Zitian Gao, Qingnan Ren, et al.*2025 · 103 citations*](https://md.research.elamur.ai/free/en/papers/xie-2025-logic-rl-unleashing-llm/)
39. [**Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling**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/)
40. [**New Trends for Modern Machine Translation with Large Reasoning Models**Sinuo Liu, Chenyang Lyu, Minghao Wu, et al.*2026 · 16 citations*](https://md.research.elamur.ai/free/en/papers/liu-2026-new-trends-modern-machine-translation/)
41. [**When To Solve, When To Verify: Compute-Optimal Problem Solving and Generative Verification for LLM Reasoning**Nishad Singhi, Hritik Bansal, Arian Hosseini, et al.*2025 · 12 citations*](https://md.research.elamur.ai/free/en/papers/singhi-2025-solve-verify-compute-optimal-problem/)
42. [**Can LLMs Evaluate What They Cannot Annotate? Revisiting LLM Reliability in Hate Speech Detection**Paloma Piot, David Otero, Patricia Martin-Rodilla, et al.*2026 · 5 citations*](https://md.research.elamur.ai/free/en/papers/piot-2026-can-llms-evaluate-what-they/)
43. [**ARB: A Comprehensive Arabic Multimodal Reasoning Benchmark**Sara Ghaboura, Shubham Patle, Ketan More, et al.*2026 · 3 citations*](https://md.research.elamur.ai/free/en/papers/ghaboura-2026-arb-comprehensive-arabic-multimodal-reasoning/)
44. [**Culture-Aware Machine Translation in Large Language Models: Benchmarking and Investigation**Zekun Yuan, Yangfan Ye, Xiaocheng Feng, et al.*2026 · 2 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2026-culture-aware-machine-translation-large/)
45. [**Dicta-LM 3.0: Advancing The Frontier of Hebrew Sovereign LLMs**Shaltiel Shmidman, Avi Shmidman, Amir DN Cohen, et al.*2026 · 2 citations*](https://md.research.elamur.ai/free/en/papers/shmidman-2026-dicta-lm-3-0-advancing/)
46. [**The Riddle of Reflection: Evaluating Reasoning and Self-Awareness in Multilingual LLMs Using Indian Riddles**Abhinav P M, Ojasva Saxena, Oswald C, et al.*2026 · 2 citations*](https://md.research.elamur.ai/free/en/papers/m-2026-riddle-reflection-evaluating-reasoning-self/)
47. [**UrBLiMP: A Benchmark for Evaluating the Linguistic Competence of Large Language Models in Urdu**Farah Adeeba, Brian Dillon, Hassan Sajjad, et al.*2026 · 2 citations*](https://md.research.elamur.ai/free/en/papers/adeeba-2026-urblimp-benchmark-evaluating-linguistic-competence/)
48. [**Can LLMs Translate Italy's Language Varieties?**Edoardo Signoroni, Pavel Rychlý*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/signoroni-2026-can-llms-translate-italy-s/)
49. [**Conversational Assistants to Support Patients with Heart Failure: Comparing a Neurosymbolic Architecture with GPT**Anuja Tayal, Devika Salunke, Barbara Di Eugenio, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/tayal-2026-conversational-assistants-support-patients-heart/)
50. [**Datasets for Verb Alternations across Languages: BLM Templates and Data Augmentation Strategies**Giuseppe Samo, Paola Merlo*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/samo-2026-datasets-verb-alternations-across-languages/)
51. [**GIL-Zaragoza at SemEval 2026 Task 11: Comparing Classification, Autoformalization, and Ontologies for Formal Reasoning Capabilities**Francisco Lopez-Ponce, Lucia Pitarch, Iván Saavedra Martínez, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/ponce-2026-gil-zaragoza-semeval-2026-task/)
52. [**Is This Idea Novel? An Automated Benchmark for Judgment of Research Ideas**Tim Schopf, Michael Färber*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/schopf-2026-is-this-idea-novel-automated/)
53. [**Reformulate and Create, Don't Translate: Creating Natural Prompts for Underserved Languages**Annika Simonsen, Mathias Stenlund, Lars Bungum, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/simonsen-2026-reformulate-create-don-t-translate/)
54. [**Symbolic Grounding Reveals Representational Bottlenecks in Abstract Visual Reasoning**Mohit Vaishnav, Tanel Tammet*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/vaishnav-2026-symbolic-grounding-reveals-representational-bottlenecks/)
55. [**Synthetic Function Demonstrations Improve Generation in Low-Resource Programming Languages**Nick McKenna, Xinnuo Xu, Jack Williams, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/mckenna-2026-synthetic-function-demonstrations-improve-generation/)
56. [**Babies Learn to Look Ahead: Multi-Token Prediction in Small LMs**Ansar Aynetdinov, Alan Akbik*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/aynetdinov-2025-babies-learn-look-ahead-multi/)
57. [**HAMSA: Hijacking Aligned Compact Models via Stealthy Automation**Alexey Krylov, Iskander Vagizov, Dmitrii Korzh, et al.*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/krylov-2025-hamsa-hijacking-aligned-compact-models/)
58. [**A Survey on Parameter-efficient Fine-tuning of Large Models: Techniques, Trends, and Challenges (大模型参数高效微调方法综述: 技术、趋势与挑战)**Anda Tang (唐岸达), Zhouchen Lin (林宙辰)*2026*](https://md.research.elamur.ai/free/en/papers/tang-2026-parameter-efficient-fine/)
59. [**AmDi - Ambiguous Words Diachronic Dataset**Felix Thielen, Kai Kugler*2026*](https://md.research.elamur.ai/free/en/papers/thielen-2026-amdi-ambiguous-words-diachronic-dataset/)
60. [**AraDynFact: Dynamic Evaluation of Factual Knowledge in Arabic**Ignacio Iacobacci, Faroq Altam, Zhaozhi Qian, et al.*2026*](https://md.research.elamur.ai/free/en/papers/iacobacci-2026-aradynfact-dynamic-evaluation-factual-knowledge/)
61. [**Breaking the Benchmark: Revealing LLM Bias via Minimal Contextual Augmentation**Kaveh Eskandari Miandoab, Mahammed Kamruzzaman, Arshia Gharooni, et al.*2026*](https://md.research.elamur.ai/free/en/papers/miandoab-2026-breaking-benchmark-revealing-llm-bias/)
62. [**DeepICD-R1: Medical Reasoning through Hierarchical Rewards and Unsupervised Distillation**Tom Röhr, Thomas Maximilian Josef Steffek, Roman Teucher, et al.*2026*](https://md.research.elamur.ai/free/en/papers/rohr-2026-deepicd-r1-medical-reasoning-through/)
63. [**Do Multimodal LLMs Understand Order? Measuring the Fragility of Multimodal Reasoning under Input Order Perturbations**Sheng-Lun Wei, Yu-Ling Liao, Hen-Hsen Huang, et al.*2026*](https://md.research.elamur.ai/free/en/papers/wei-2026-do-multimodal-llms-understand-order/)
64. [**E-CONAN (Entailment, CONtradition And Neutral) Benchmarks: Arabic Textual Entailment and Natural Inference Datasets**Khloud AL Jallad, Nada Ghneim, Ghaida Rebdawi*2026*](https://md.research.elamur.ai/free/en/papers/jallad-2026-e-conan-entailment-contradition-neutral-benchmarks/)
65. [**GhostWriter: Hidden AI-Generated Texts over Multiple Languages, Domains and Generators**Manuel Schaaf, Kevin Bönisch, Alexander Mehler*2026*](https://md.research.elamur.ai/free/en/papers/schaaf-2026-ghostwriter-hidden-ai-generated-texts/)
66. [**Hindsight Quality Prediction Experiments in Multi-Candidate Human-Post-Edited Machine Translation**Malik Marmonier, Benoît Sagot, Rachel Bawden*2026*](https://md.research.elamur.ai/free/en/papers/marmonier-2026-hindsight-quality-prediction-experiments-multi/)
67. [**ID10M-JAM: Stress-Testing Idiom Identification Under Challenging Context**Kai Golan Hashiloni, Lior Livyatan, Ofri Hefetz, et al.*2026*](https://md.research.elamur.ai/free/en/papers/hashiloni-2026-id10m-jam-stress-testing-idiom/)
68. [**Leveraging Digitized Newspapers to Collect Summarization Data in Low-Resource Languages**Noam Dahan, Omer Kidron, Gabriel Stanovsky*2026*](https://md.research.elamur.ai/free/en/papers/dahan-2026-leveraging-digitized-newspapers-collect-summarization/)
69. [**Meaning Over Morphology: A Multi-Metric Benchmark of LLMs for Bangla Dialect Translation**Soumik Deb Niloy, Subhey Sadi Rahman, Mahbub E Sobhani, et al.*2026*](https://md.research.elamur.ai/free/en/papers/niloy-2026-meaning-over-morphology-multi-metric/)
70. [**mSCoRe: A Multilingual and Scalable Benchmark for Skill-based Commonsense Reasoning**Nghia Trung Ngo, Franck Dernoncourt, Thien Huu Nguyen*2026*](https://md.research.elamur.ai/free/en/papers/ngo-2026-mscore-multilingual-scalable-benchmark-skill/)
71. [**Multi-Agent Debate for Machine Translation: A Case Study on English-Japanese Translation**Zhan Shen, Jason Naradowsky, Xiaotian Wang, et al.*2026*](https://md.research.elamur.ai/free/en/papers/shen-2026-multi-agent-debate-machine-translation/)
72. [**Multimodal LLMs Do Not Compose Skills Optimally across Modalities**Paula Ontalvilla, Aitor Ormazabal, Gorka Azkune*2026*](https://md.research.elamur.ai/free/en/papers/ontalvilla-2026-multimodal-llms-do-not-compose/)
73. [**Quantifying Metric and Model Agreement in Bias Evaluation of Large Language Models**Arash Asgari, Huan Wu, Amirreza Naziri, et al.*2026*](https://md.research.elamur.ai/free/en/papers/asgari-2026-quantifying-metric-model-agreement-bias/)
74. [**SPARTA: Evaluating Reasoning Segmentation Robustness through Black-Box Adversarial Paraphrasing in Text Autoencoder Latent Space**Viktoriia Zinkovich, Anton Antonov, Andrei Spiridonov, et al.*2026*](https://md.research.elamur.ai/free/en/papers/zinkovich-2026-sparta-evaluating-reasoning-segmentation-robustness/)
75. [**Stands to Reason: Investigating the Effect of Reasoning on Idiomaticity Detection**Dylan Phelps, Rodrigo Wilkens, Edward Gow-Smith, et al.*2026*](https://md.research.elamur.ai/free/en/papers/phelps-2026-stands-reason-investigating-effect-reasoning/)
76. [**STRUDEL: Unrolling a Benchmark for Evaluating Vision-Language Models on Structured Diagram Understanding across Domains**Daniel Steinigen, Lucie Flek, Sebastian Houben*2026*](https://md.research.elamur.ai/free/en/papers/steinigen-2026-strudel-unrolling-benchmark-evaluating-vision/)
77. [**The Surge of Anti-Semitism in German Social Media following the October 7 Attacks**Gregor Wiedemann, Daniel Wehrend*2026*](https://md.research.elamur.ai/free/en/papers/wiedemann-2026-surge-anti-semitism-german-social/)
78. [**Uncovering Hidden Violent Tendencies in LLMs: A Demographic Analysis via Behavioral Vignettes**Quintin Myers, Yanjun Gao*2026*](https://md.research.elamur.ai/free/en/papers/myers-2026-uncovering-hidden-violent-tendencies-llms/)
79. [**When the Database Fails: Prompting LLM Dialogue Agents for Safe Recovery in Task-Oriented Dialogue**Mohammad Alijanpour Shalmani, Alale Rezvani Boroujeni, Jiann S. Yuan*2026*](https://md.research.elamur.ai/free/en/papers/shalmani-2026-when-database-fails-prompting-llm/)
80. [**Survey of Tool Use in Large Language Models (大语言模型的工具使用综述)**Yining Zheng (郑逸宁), Zhen Yu (余镇), Bufan Li (李不凡), et al.*2025*](https://md.research.elamur.ai/free/en/papers/zheng-2025-tool-use-large/)

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