🔗 Who cites «Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws»

47 papers of our library cite it (of the 140,884 whose bibliographies we read) · 10 with a page here, the most cited first · ← the paper

1. Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model ParametersCharlie Snell, Jaehoon Lee, Kelvin Xu, et al.2024 · 524 citations
2. Yi: Open Foundation Models by 01.AIAlex Young, Bei Chen, Chao Li, et al.2024 · 402 citations
3. MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training StrategiesShengding Hu, Yuge Tu, Xu Han, et al.2024 · 258 citations
4. Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language ModelsYangzhen Wu, Zhiqing Sun, Shanda Li, et al.2024 · 114 citations
5. Scaling Laws with Vocabulary: Larger Models Deserve Larger VocabulariesChaofan Tao, Qian Liu, Longxu Dou, et al.2024 · 42 citations
6. Scaling Laws and Compute-Optimal Training Beyond Fixed Training DurationsAlexander Hägele, Elie Bakouch, Atli Kosson, et al.2024 · 24 citations
7. Distillation Scaling LawsDan Busbridge, Amitis Shidani, Floris Weers, et al.2025 · 23 citations
8. Resolving Discrepancies in Compute-Optimal Scaling of Language ModelsTomer Porian, Mitchell Wortsman, Jenia Jitsev, et al.2024 · 14 citations
9. Chinchilla Scaling: A replication attemptTamay Besiroglu, Ege Erdil, Matthew Barnett, et al.2024 · 12 citations
10. Scaling Laws for Code: A More Data-Hungry RegimeXianzhen Luo, Wenzhen Zheng, Qingfu Zhu, et al.2026 · 3 citations

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

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