---
title: "Who cites Cody Hao Yu"
description: "2,858 papers of the Elamur Research library cite the works of Cody Hao Yu, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/cody-hao-yu/cited-by/"
---
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# Who cites Cody Hao Yu

3,316 citations of their works from 2,858 papers of our library (of the 140,884 whose bibliographies we read) · 73 with a page here, the most cited first · [← Cody Hao Yu](https://md.research.elamur.ai/free/en/authors/cody-hao-yu/)

1. [**DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning**DeepSeek-AI, Daya Guo, Dejian Yang, et al.*2025 · 4,593 citations*](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2025-deepseek-r1-incentivizing-reasoning-capability/)
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. [**DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models**Zhihong Shao, Peiyi Wang, Qihao Zhu, et al.*2024 · 2,013 citations*](https://md.research.elamur.ai/free/en/papers/shao-2024-deepseekmath-pushing-limits-mathematical-reasoning/)
4. [**Qwen Technical Report**Jinze Bai, Shuai Bai, Yunfei Chu, et al.*2023 · 1,643 citations*](https://md.research.elamur.ai/free/en/papers/bai-2023-qwen-technical-report/)
5. [**Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone**Marah Abdin, Jyoti Aneja, Hany Awadalla, et al.*2024 · 1,113 citations*](https://md.research.elamur.ai/free/en/papers/abdin-2024-phi-3-technical-report-highly/)
6. [**LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models**Yaowei Zheng, Richong Zhang, Junhao Zhang, et al.*2024 · 1,035 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2024-llamafactory-unified-efficient-fine-tuning/)
7. [**SGLang: Efficient Execution of Structured Language Model Programs**Lianmin Zheng, Liangsheng Yin, Zhiqiang Xie, et al.*2023 · 710 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2023-sglang-efficient-execution-structured-language/)
8. [**Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection**Akari Asai, Zeqiu Wu, Yizhong Wang, et al.*2023 · 662 citations*](https://md.research.elamur.ai/free/en/papers/asai-2023-self-rag-learning-retrieve-generate/)
9. [**HybridFlow: A Flexible and Efficient RLHF Framework**Guangming Sheng, Chi Zhang, Zilingfeng Ye, et al.*2024 · 598 citations*](https://md.research.elamur.ai/free/en/papers/sheng-2024-hybridflow-flexible-efficient-rlhf-framework/)
10. [**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/)
11. [**LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code**Naman Jain, King Han, Alex Gu, et al.*2024 · 540 citations*](https://md.research.elamur.ai/free/en/papers/jain-2024-livecodebench-holistic-contamination-free-evaluation/)
12. [**Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations**Peiyi Wang, Lei Li, Zhihong Shao, et al.*2024 · 482 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-math-shepherd-verify-reinforce-llms/)
13. [**Kimi k1.5: Scaling Reinforcement Learning with LLMs**Kimi Team, Angang Du, Bofei Gao, et al.*2025 · 413 citations*](https://md.research.elamur.ai/free/en/papers/kimi-team-2025-kimi-k1-5-scaling-reinforcement/)
14. [**Yi: Open Foundation Models by 01.AI**Alex Young, Bei Chen, Chao Li, et al.*2024 · 402 citations*](https://md.research.elamur.ai/free/en/papers/ai-2024-yi-open-foundation-models-01/)
15. [**Tulu 3: Pushing Frontiers in Open Language Model Post-Training**Nathan Lambert, Jacob Morrison, Valentina Pyatkin, et al.*2024 · 379 citations*](https://md.research.elamur.ai/free/en/papers/lambert-2024-tulu-3-pushing-frontiers-open/)
16. [**An Image is Worth 1/2 Tokens After Layer 2: Plug-and-Play Inference Acceleration for Large Vision-Language Models**Liang Chen, Haozhe Zhao, Tianyu Liu, et al.*2024 · 276 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-image-worth-1-2-tokens/)
17. [**Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads**Tianle Cai, Yuhong Li, Zhengyang Geng, et al.*2024 · 271 citations*](https://md.research.elamur.ai/free/en/papers/cai-2024-medusa-simple-llm-inference-acceleration/)
18. [**HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models**Bernal Jimenez Gutierrez, Yiheng Shu, Yu Gu, et al.*2024 · 264 citations*](https://md.research.elamur.ai/free/en/papers/gutierrez-2024-hipporag-neurobiologically-inspired-long-term/)
19. [**MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies**Shengding Hu, Yuge Tu, Xu Han, et al.*2024 · 258 citations*](https://md.research.elamur.ai/free/en/papers/hu-2024-minicpm-unveiling-potential-small-language/)
20. [**Chatlaw: A Multi-Agent Legal Assistant based on a Role-Aligned Mixture-of-Experts Architecture**Jiaxi Cui; Munan Ning; Zongjian Li; Bohua Chen; Yang Yan; Hao Li; Bin Ling; Yonghong Tian;, Li Yuan*2023 · 246 citations*](https://md.research.elamur.ai/free/en/papers/cui-2023-chatlaw-multi-agent-legal-assistant/)
21. [**KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache**Zirui Liu, Jiayi Yuan, Hongye Jin, et al.*2024 · 243 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-kivi-tuning-free-asymmetric-2bit/)
22. [**Large Language Monkeys: Scaling Inference Compute with Repeated Sampling**Bradley Brown, Jordan Juravsky, Ryan Ehrlich, et al.*2024 · 241 citations*](https://md.research.elamur.ai/free/en/papers/brown-2024-large-language-monkeys-scaling-inference/)
23. [**Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch**Le Yu, Bowen Yu, Haiyang Yu, et al.*2023 · 209 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-language-models-super-mario-absorbing/)
24. [**Quest: Query-Aware Sparsity for Efficient Long-Context LLM Inference**Jiaming Tang, Yilong Zhao, Kan Zhu, et al.*2024 · 197 citations*](https://md.research.elamur.ai/free/en/papers/tang-2024-quest-query-aware-sparsity-efficient/)
25. [**Refusal in Language Models Is Mediated by a Single Direction**Andy Arditi, Oscar Obeso, Aaquib Syed, et al.*2024 · 188 citations*](https://md.research.elamur.ai/free/en/papers/arditi-2024-refusal-language-models-mediated-single/)
26. [**BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions**Terry Yue Zhuo, Minh Chien Vu, Jenny Chim, et al.*2024 · 163 citations*](https://md.research.elamur.ai/free/en/papers/zhuo-2024-bigcodebench-benchmarking-code-generation-diverse/)
27. [**Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding**Jun Zhang, Jue Wang, Huan Li, et al.*2024 · 146 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-draft-verify-lossless-large-language/)
28. [**Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding**Heming Xia, Zhe Yang, Qingxiu Dong, et al.*2024 · 145 citations*](https://md.research.elamur.ai/free/en/papers/xia-2024-unlocking-efficiency-large-language-model/)
29. [**Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing**Zhangchen Xu, Fengqing Jiang, Luyao Niu, et al.*2024 · 144 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-magpie-alignment-data-synthesis-scratch/)
30. [**To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning**Zayne Sprague, Fangcong Yin, Juan Diego Rodriguez, et al.*2024 · 133 citations*](https://md.research.elamur.ai/free/en/papers/sprague-2024-cot-cot-chain-thought-helps/)
31. [**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/)
32. [**InfLLM: Training-Free Long-Context Extrapolation for LLMs with an Efficient Context Memory**Chaojun Xiao, Pengle Zhang, Xu Han, et al.*2024 · 125 citations*](https://md.research.elamur.ai/free/en/papers/xiao-2024-infllm-training-free-long-context/)
33. [**AlphaMath Almost Zero: Process Supervision without Process**Guoxin Chen, Minpeng Liao, Chengxi Li, et al.*2024 · 119 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-alphamath-almost-zero-process-supervision/)
34. [**ProcessBench: Identifying Process Errors in Mathematical Reasoning**Chujie Zheng, Zhenru Zhang, Beichen Zhang, et al.*2025 · 116 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2025-processbench-identifying-process-errors-mathematical/)
35. [**Qwen3-Omni Technical Report**Jin Xu, Zhifang Guo, Hangrui Hu, et al.*2025 · 114 citations*](https://md.research.elamur.ai/free/en/papers/xu-2025-qwen3-omni-technical-report/)
36. [**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/)
37. [**DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search**Huajian Xin, Z. Z. Ren, Junxiao Song, et al.*2024 · 113 citations*](https://md.research.elamur.ai/free/en/papers/xin-2024-deepseek-prover-v1-5-harnessing/)
38. [**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/)
39. [**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/)
40. [**DuoAttention: Efficient Long-Context LLM Inference with Retrieval and Streaming Heads**Guangxuan Xiao, Jiaming Tang, Jingwei Zuo, et al.*2024 · 109 citations*](https://md.research.elamur.ai/free/en/papers/xiao-2024-duoattention-efficient-long-context-llm/)
41. [**Qwen2.5-1M Technical Report**An Yang, Bowen Yu, Chengyuan Li, et al.*2025 · 100 citations*](https://md.research.elamur.ai/free/en/papers/yang-2025-qwen2-5-1m-technical/)
42. [**VariErr NLI: Separating Annotation Error from Human Label Variation**Leon Weber-Genzel, Siyao Peng, Marie-Catherine De Marneffe, et al.*2024 · 51 citations*](https://md.research.elamur.ai/free/en/papers/genzel-2024-varierr-nli-separating-annotation-error/)
43. [**Observational Scaling Laws and the Predictability of Language Model Performance**Yangjun Ruan, Chris J. Maddison, Tatsunori Hashimoto*2024 · 28 citations*](https://md.research.elamur.ai/free/en/papers/ruan-2024-observational-scaling-laws-predictability-language/)
44. [**Skywork-Math: Data Scaling Laws for Mathematical Reasoning in Large Language Models — The Story Goes On**Liang Zeng, Liangjun Zhong, Liang Zhao, et al.*2024 · 14 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2024-skywork-math-data-scaling-laws/)
45. [**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/)
46. [**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/)
47. [**KCIF: Knowledge-Conditioned Instruction Following**Rudra Murthy, Praveen Venkateswaran, Prince Kumar, et al.*2026 · 2 citations*](https://md.research.elamur.ai/free/en/papers/murthy-2026-kcif-knowledge-conditioned-instruction-following/)
48. [**Remedy-R: Generative Reasoning for Machine Translation Evaluation without Error Annotations**Shaomu Tan, Ryosuke Mitani, Ritvik Choudhary, et al.*2026 · 2 citations*](https://md.research.elamur.ai/free/en/papers/tan-2026-remedy-r-generative-reasoning-machine/)
49. [**Can Large Language Models Predict the Outcome of Judicial Decisions?**Mohamed Bayan Kmainasi, Ali Ezzat Shahroor, Amani Al-Ghraibah*2025 · 2 citations*](https://md.research.elamur.ai/free/en/papers/kmainasi-2025-large-language-models-predict-outcome/)
50. [**Addressing the Ecological Fallacy in Larger LMs with Human Context**Nikita Soni, Dhruv Vijay Kunjadiya, Pratham Piyush Shah, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/soni-2026-addressing-ecological-fallacy-larger-lms/)
51. [**CLASE: A Hybrid Method for Chinese Legalese Stylistic Evaluation**Yiran Rex Ma, Yuxiao Ye, Huiyuan Xie*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/ma-2026-clase-hybrid-method-chinese-legalese/)
52. [**German General Social Survey Personas: A Survey-Derived Persona Prompt Collection for Population-Aligned LLM Studies**Jens Rupprecht, Leon Froehling, Claudia Wagner, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/rupprecht-2026-german-general-social-survey-personas/)
53. [**Plan-Guided Text Simplification with Extended Contexts**Pascal Mathas, Jan Bakker, Jaap Kamps*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/mathas-2026-plan-guided-text-simplification-extended/)
54. [**Sense and Sensitivity: “Reasoning” Models are More Robust, but can Diverge from Human Consensus in a Legal Interpretation Task**Dawson Petersen, Abhishek Purushothama, Nathan Schneider*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/petersen-2026-sense-sensitivity-reasoning-models-are/)
55. [**A Dialogue System for Second Language Learning with Dynamic Adaptation to In-Dialogue Difficulty Changes**Taku Morioka, Junya Takayama, Tomoyuki Kajiwara*2026*](https://md.research.elamur.ai/free/en/papers/morioka-2026-dialogue-system-second-language-learning/)
56. [**Augmenting LLM Reasoning with Dynamic Notes Writing for Complex MultiHop QA**Rishabh Maheshwary, Masoud Hashemi, Khyati Mahajan, et al.*2026*](https://md.research.elamur.ai/free/en/papers/maheshwary-2026-augmenting-llm-reasoning-dynamic-notes/)
57. [**Beyond Catalogue Counts: The Dataset Visibility Asymmetry in Low-Resource Multilingual NLP**Zhiyin Tan, Changxu Duan*2026*](https://md.research.elamur.ai/free/en/papers/tan-2026-beyond-catalogue-counts-dataset-visibility/)
58. [**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/)
59. [**Detecting Latin in Historical Books with Large Language Models: A Multimodal Benchmark**Yu Wu, Ke Shu, Jonas Fischer, et al.*2026*](https://md.research.elamur.ai/free/en/papers/wu-2026-detecting-latin-historical-books-large/)
60. [**Evaluating Discriminability of Vision-Language Models**Masayasu Muraoka, Naoaki Okazaki*2026*](https://md.research.elamur.ai/free/en/papers/muraoka-2026-evaluating-discriminability-vision-language-models/)
61. [**Generation of Instruction and Preference Dataset for Improving Japanese Instruction Following in LLMs**Kei Moriyama, Takashi Kodama, Kouta Nakayama*2026*](https://md.research.elamur.ai/free/en/papers/moriyama-2026-generation-instruction-preference-dataset-improving/)
62. [**GlossMATE: Multi-Agent Translator Explanations for Glosses**Changbing Yang, Patrick Littell, Gabriel Bernier-Colborne, et al.*2026*](https://md.research.elamur.ai/free/en/papers/yang-2026-glossmate-multi-agent-translator-explanations/)
63. [**I Came, I Saw, I Explained: Benchmarking Multimodal LLMs on Figurative Meaning in Memes**Shijia Zhou, Saif M. Mohammad, Barbara Plank, et al.*2026*](https://md.research.elamur.ai/free/en/papers/zhou-2026-i-came-i-saw-i/)
64. [**Information Asymmetry across Language Varieties: A Case Study on Cantonese-Mandarin and Bavarian-German QA**Renhao Pei, Siyao Peng, Verena Blaschke, et al.*2026*](https://md.research.elamur.ai/free/en/papers/pei-2026-information-asymmetry-across-language-varieties/)
65. [**InstructSum: A Benchmark to Evaluate Instruction-Following Capability of Large Language Models in Summarization**Kosuke Nishida, Kyosuke Nishida, Itsumi Saito*2026*](https://md.research.elamur.ai/free/en/papers/nishida-2026-instructsum-benchmark-evaluate-instruction-following/)
66. [**Orthographic Constraint Satisfaction and Human Difficulty Alignment in Large Language Models**Bryan E. Tuck, Rakesh Verma*2026*](https://md.research.elamur.ai/free/en/papers/tuck-2026-orthographic-constraint-satisfaction-human-difficulty/)
67. [**Russian Generative Spelling, Punctuation and Capitalization Correction**Nikita Martynov, Danil Astafurov, Ulyana Isaeva, et al.*2026*](https://md.research.elamur.ai/free/en/papers/martynov-2026-russian-generative-spelling-punctuation-capitalization/)
68. [**Simplify-Pro: A Two-level and Progressive LLM-based Framework for Auto Long Text Simplification**Peng Zhou, Guangxin Li, Xiaoying Huang, et al.*2026*](https://md.research.elamur.ai/free/en/papers/zhou-2026-simplify-pro-two-level-progressive/)
69. [**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/)
70. [**The MaTOS Pipeline for the Translation of Scientific Abstracts on the HAL Platform**Panagiotis Tsolakis, Ziqian Peng, Laurent Romary, et al.*2026*](https://md.research.elamur.ai/free/en/papers/tsolakis-2026-matos-pipeline-translation-scientific-abstracts/)
71. [**When Similar Means Different: Evaluating LLMs on Arabic–Hebrew Cognates**Junhong Liang, Noor Abo Mokh, Bashar Alhafni*2026*](https://md.research.elamur.ai/free/en/papers/liang-2026-similar-means-different-evaluating-llms/)
72. [**When the Gold Standard Isn't Necessarily Standard: Challenges of Evaluating the Translation of User-Generated Content**Lydia Nishimwe, Benoît Sagot, Rachel Bawden*2026*](https://md.research.elamur.ai/free/en/papers/nishimwe-2026-when-gold-standard-isn-t/)
73. [**Why do Large Language Models Fail in Low-resource Translation? Unraveling the Token Dynamics of Large Language Models for Machine Translation**Shenbin Qian, Yves Scherrer*2026*](https://md.research.elamur.ai/free/en/papers/qian-2026-why-do-large-language-models/)

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