--- title: "Who cites DeepSeek-Coder-V2: Breaking the Barrier of…" description: "144 papers of the Elamur Research library cite «DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence», found in their…" canonical: "https://research.elamur.ai/free/en/papers/deepseek-ai-2024-deepseek-coder-v2-breaking-barrier/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/deepseek-ai-2024-deepseek-coder-v2-breaking-barrier/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [DeepSeek-Coder-V2: Breaking the Barrier of…](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2024-deepseek-coder-v2-breaking-barrier/) / cited by # Who cites «DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence» 144 papers of our library cite it (of the 140,884 whose bibliographies we read) · 14 with a page here, the most cited first · [← the paper](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2024-deepseek-coder-v2-breaking-barrier/) 1. [**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/) 2. [**Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement**An Yang, Beichen Zhang, Binyuan Hui, et al.*2024 · 410 citations*](https://md.research.elamur.ai/free/en/papers/yang-2024-qwen2-5-math-technical-report/) 3. [**A Survey on Large Language Models for Code Generation**Juyong Jiang, Fan Wang, Jiasi Shen, et al.*2024 · 366 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2024-survey-large-language-models-code/) 4. [**The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery**Chris Lu, Cong Lu, Robert Tjarko Lange, et al.*2024 · 362 citations*](https://md.research.elamur.ai/free/en/papers/lu-2024-ai-scientist-fully-automated/) 5. [**From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline**Tianle Li, Wei-Lin Chiang, Evan Frick, et al.*2024 · 191 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-crowdsourced-data-high-quality-benchmarks/) 6. [**Scaling Synthetic Data Creation with 1,000,000,000 Personas**Tao Ge, Xin Chan, Xiaoyang Wang, et al.*2024 · 160 citations*](https://md.research.elamur.ai/free/en/papers/ge-2024-scaling-synthetic-data-creation-1/) 7. [**InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency**Weiyun Wang, Zhangwei Gao, Lixin Gu, et al.*2025 · 159 citations*](https://md.research.elamur.ai/free/en/papers/wang-2025-internvl3-5-advancing-open/) 8. [**Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models**Bofei Gao, Feifan Song, Zhe Yang, et al.*2024 · 159 citations*](https://md.research.elamur.ai/free/en/papers/gao-2024-omni-math-universal-olympiad-level/) 9. [**The Lessons of Developing Process Reward Models in Mathematical Reasoning**Zhenru Zhang, Chujie Zheng, Yangzhen Wu, et al.*2025 · 155 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2025-lessons-developing-process-reward-models/) 10. [**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/) 11. [**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/) 12. [**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/) 13. [**Scaling Laws for Code: A More Data-Hungry Regime**Xianzhen Luo, Wenzhen Zheng, Qingfu Zhu, et al.*2026 · 3 citations*](https://md.research.elamur.ai/free/en/papers/luo-2026-scaling-laws-code-more-data/) 14. [**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/) 130 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2024-deepseek-coder-v2-breaking-barrier/) — DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence