--- title: "Who cites PyTorch 2: Faster Machine — 256 citing papers" description: "256 papers of the Elamur Research library cite «PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and…" canonical: "https://research.elamur.ai/free/en/papers/ansel-2024-pytorch-2-faster-machine-learning/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/ansel-2024-pytorch-2-faster-machine-learning/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [PyTorch 2: Faster Machine Learning Through…](https://md.research.elamur.ai/free/en/papers/ansel-2024-pytorch-2-faster-machine-learning/) / cited by # Who cites «PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation» 256 papers of our library cite it (of the 149,439 whose bibliographies we read) · 19 with a page here, the most cited first · [← back to «PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation»](https://md.research.elamur.ai/free/en/papers/ansel-2024-pytorch-2-faster-machine-learning/) 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. [**Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution arXiv:2409.12191**Peng Wang, Shuai Bai, Sinan Tan, et al.*2024 · 860 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-qwen2-vl-enhancing-vision/) 3. [**Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference arXiv:2412.13663**Benjamin Warner, Antoine Chaffin, Benjamin Clavié, et al.*2024 · 358 citations*](https://md.research.elamur.ai/free/en/papers/warner-2024-smarter-better-faster-longer-modern/) 4. **[DataComp-LM: In search of the next generation of training sets for language models](https://md.research.elamur.ai/free/en/papers/li-2024-datacomp-lm-search-next-generation/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jeffrey Li, Alex Fang, Georgios Smyrnis, et al.*2024 · 191 citations* 5. [**FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving MLSys 2025**Zihao Ye, Lequn Chen, Ruihang Lai, et al.*2025 · 177 citations*](https://md.research.elamur.ai/free/en/papers/ye-2025-flashinfer-efficient-customizable-attention-engine/) 6. [**Liger Kernel: Efficient Triton Kernels for LLM Training arXiv:2410.10989**Pin-Lun Hsu, Yun Dai, Vignesh Kothapalli, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/hsu-2024-liger-kernel-efficient-triton-kernels/) 7. [**DART-Math: Difficulty-Aware Rejection Tuning for Mathematical Problem-Solving arXiv:2407.13690**Yuxuan Tong, Xiwen Zhang, Rui Wang, et al.*2024 · 81 citations*](https://md.research.elamur.ai/free/en/papers/tong-2024-dart-math-difficulty-aware-rejection/) 8. [**Flex Attention: A Programming Model for Generating Optimized Attention Kernels arXiv:2412.05496**Juechu Dong, Boyuan Feng, Driss Guessous, et al.*2024 · 55 citations*](https://md.research.elamur.ai/free/en/papers/dong-2024-flex-attention-programming-model-generating/) 9. [**Language models scale reliably with over-training and on downstream tasks arXiv:2403.08540**Samir Yitzhak Gadre, Georgios Smyrnis, Vaishaal Shankar, et al.*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/gadre-2024-language-models-scale-reliably-over/) 10. [**Kimi K3: Open Frontier Intelligence arXiv:2607.24653**Kimi Team, Tongtong Bai, Yifan Bai, et al.*2026 · 40 citations*](https://md.research.elamur.ai/free/en/papers/kimi-team-2026-kimi-k3-open-frontier/) 11. [**EuroBERT: Scaling Multilingual Encoders for European Languages arXiv:2503.05500**Nicolas Boizard, Hippolyte Gisserot-Boukhlef, Duarte M. Alves, et al.*2025 · 34 citations*](https://md.research.elamur.ai/free/en/papers/boizard-2025-eurobert-scaling-multilingual-encoders-european/) 12. [**JetMoE: Reaching Llama2 Performance with 0.1M Dollars arXiv:2404.07413**Yikang Shen, Zhen Guo, Tianle Cai, et al.*2024 · 28 citations*](https://md.research.elamur.ai/free/en/papers/shen-2024-jetmoe-reaching-llama2-performance-0/) 13. **[Mitigating Catastrophic Forgetting in Target Language Adaptation of LLMs via Source-Shielded Updates](https://md.research.elamur.ai/free/en/papers/yamaguchi-2026-mitigating-catastrophic-forgetting-target-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Atsuki Yamaguchi, Terufumi Morishita, Aline Villavicencio, et al.*2026 · 3 citations* 14. **[Beyond Coherence: Improving Temporal Consistency and Interpretability in Dynamic Topic Models](https://md.research.elamur.ai/free/en/papers/nguyen-2026-beyond-coherence-improving-temporal-consistency/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Thanh Vinh Nguyen, Ngo Van Dong, Minh Chu Xuan, et al.*2026 · 1 citation* 15. **[Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks](https://md.research.elamur.ai/free/en/papers/yamaguchi-2026-enhancing-linguistic-competence-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Atsuki Yamaguchi, Maggie Mi, Nikolaos Aletras*2026 · 1 citation* 16. **[NeoAMT: Neologism-Aware Agentic Machine Translation with Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/miao-2026-neoamt-neologism-aware-agentic-machine/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhongtao Miao, Kaiyan Zhao, Masaaki Nagata, et al.*2026 · 1 citation* 17. **[Detecting Sensitive Personal Information in Japanese Pre-Training Corpora for Large Language Models](https://md.research.elamur.ai/free/en/papers/minamoto-2026-detecting-sensitive-personal-information-japanese/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Rei Minamoto, Yusuke Oda, Daisuke Kawahara*2026* 18. **[Entity-Level Sentiment Analysis with Sentence Relevance Detection](https://md.research.elamur.ai/free/en/papers/ronningstad-2026-entity-level-sentiment-analysis-sentence/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Egil Rønningstad, Roman Klinger, Lilja Øvrelid, et al.*2026* 19. **[High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing](https://md.research.elamur.ai/free/en/papers/wang-2026-high-throughput-memory-efficient-zeroth/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Liangyu Wang, Huanyi Xie, Di Wang*2026* 237 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/ansel-2024-pytorch-2-faster-machine-learning/) — PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation