🔗 Who cites «Maximize Your Data's Potential: Enhancing LLM Accuracy with Two-Phase Pretraining»

12 papers of our library cite it (of the 149,439 whose bibliographies we read) · 5 with a page here, the most cited first · ← back to «Maximize Your Data's Potential: Enhancing LLM Accuracy with Two-Phase Pretraining»

1. Nemotron-CC: Transforming Common Crawl into a Refined Long-Horizon Pretraining DatasetAnnual Meeting of the Association for Computational Linguistics (ACL)Dan Su, Kezhi Kong, Ying Lin, et al.2025 · 65 citations
2. 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
3. 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
4. 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
5. Babies Learn to Look Ahead: Multi-Token Prediction in Small LMsBabyLM WorkshopAnsar Aynetdinov, Alan Akbik2025 · 1 citation

7 more papers of our library cite it; they have no page here yet.

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