🔗 Who cites «Nemotron-CC: Transforming Common Crawl into a Refined Long-Horizon Pretraining Dataset»

64 papers of our library cite it (of the 149,439 whose bibliographies we read) · 22 with a page here, the most cited first · ← back to «Nemotron-CC: Transforming Common Crawl into a Refined Long-Horizon Pretraining Dataset»

1. Kimi k1.5: Scaling Reinforcement Learning with LLMsarXiv:2501.12599Kimi Team, Angang Du, Bofei Gao, et al.2025 · 431 citations
2. DataComp-LM: In search of the next generation of training sets for language modelsConference on Neural Information Processing Systems (NeurIPS)Jeffrey Li, Alex Fang, Georgios Smyrnis, et al.2024 · 191 citations
3. GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation ModelsarXiv:2508.06471Aohan Zeng, Xin Lv, Qinkai Zheng, et al.2025 · 166 citations
4. Olmo 3arXiv:2512.13961Team Olmo: Allyson Ettinger, Amanda Bertsch, Bailey Kuehl, et al.2025 · 130 citations
5. OpenThoughts: Data Recipes for Reasoning ModelsarXiv:2506.04178Etash Guha, Ryan Marten, Sedrick Keh, et al.2025 · 122 citations
6. Kimi-VL Technical ReportarXiv:2504.07491Kimi Team, Angang Du, Bohong Yin, et al.2025 · 70 citations
7. NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning ModelarXiv:2508.14444NVIDIA: Aarti Basant, Abhijit Khairnar, Abhijit Paithankar, et al.2025 · 39 citations
8. Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer ModelsarXiv:2504.03624NVIDIA: Aaron Blakeman, Aarti Basant, Abhinav Khattar, et al.2025 · 38 citations
9. Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic ReasoningarXiv:2512.20848NVIDIA: Aaron Blakeman, Aaron Grattafiori, Aarti Basant, et al.2025 · 27 citations
10. Scaling Latent Reasoning via Looped Language ModelsarXiv:2510.25741Rui-Jie Zhu, Zixuan Wang, Kai Hua, et al.2025 · 22 citations
11. Intern-S1: A Scientific Multimodal Foundation ModelarXiv:2508.15763Lei Bai, Zhongrui Cai, Yuhang Cao, et al.2025 · 19 citations
12. Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and PerformancearXiv:2507.22448Jingwei Zuo, Maksim Velikanov, Ilyas Chahed, et al.2025 · 15 citations
13. Maximize Your Data's Potential: Enhancing LLM Accuracy with Two-Phase PretrainingarXiv:2412.15285Steven Feng, Shrimai Prabhumoye, Kezhi Kong, et al.2024 · 12 citations
14. Aleph-Alpha-GermanWeb: Improving German-language LLM pre-training with model-based data curation and synthetic data generationConference of the European Chapter of the Association for Computational Linguistics (EACL)Thomas F Burns, Letitia Parcalabescu, Stephan Waeldchen, et al.2026 · 4 citations
15. Data Mixing Agent: Learning to Re-weight Domains for Continual Pre-trainingAnnual Meeting of the Association for Computational Linguistics (ACL)Kailai Yang, Xiao Liu, Lei Ji, et al.2026 · 1 citation
16. HPLT 3.0: Very Large-Scale Multilingual Resources for LLMs and MT. Mono- and Bi-lingual Data, Multilingual Evaluation, and Pre-Trained ModelsLanguage Resources and Evaluation Conference (LREC)Stephan Oepen, Nikolay Arefyev, Mikko Aulamo, et al.2026 · 1 citation
17. Dialectal Filtering: Synthesizing Kurdish Corpora for Low-Resource Varieties by Utilizing “Noise” in Large Textual DataLanguage Resources and Evaluation Conference (LREC)Christian Schuler, Raman Ahmad, Ānrán Wáng, et al.2026
18. HSS-Synth: Humanities and Social Sciences Data Synthesis for LLMsFindings of the Association for Computational LinguisticsRu Peng, Tianyu Zhao, Xijun Gu, et al.2026
19. Large-Scale Diverse Synthesis for Mid-TrainingFindings of the Association for Computational LinguisticsXuemiao Zhang, Chengying Tu, Can Ren, et al.2026
20. LinkQA: Synthesizing Diverse QA from Multiple Seeds Strongly Linked by Knowledge PointsAnnual Meeting of the Association for Computational Linguistics (ACL)Xuemiao Zhang, Can Ren, Chengying Tu, et al.2026
21. SciPedia: Unlocking the Value of Scientific Data for Pre-trainingAnnual Meeting of the Association for Computational Linguistics (ACL)Yiwei Qin, Zhen Huang, Tiantian Mi, et al.2026
22. SELECting over Tokens: Curating Pre-training Data at Scale via Token ClassificationAnnual Meeting of the Association for Computational Linguistics (ACL)Xin Tong, Weidong Zhang, Jiaang Li, et al.2026

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