--- title: "Who cites Language models scale reliably — 42 citing papers" description: "42 papers of the Elamur Research library cite «Language models scale reliably with over-training and on downstream tasks», found in their…" canonical: "https://research.elamur.ai/free/en/papers/gadre-2024-language-models-scale-reliably-over/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/gadre-2024-language-models-scale-reliably-over/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Language models scale reliably with over-training…](https://md.research.elamur.ai/free/en/papers/gadre-2024-language-models-scale-reliably-over/) / cited by # Who cites «Language models scale reliably with over-training and on downstream tasks» 42 papers of our library cite it (of the 149,439 whose bibliographies we read) · 14 with a page here, the most cited first · [← back to «Language models scale reliably with over-training and on downstream tasks»](https://md.research.elamur.ai/free/en/papers/gadre-2024-language-models-scale-reliably-over/) 1. **[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* 2. [**Rho-1: Not All Tokens Are What You Need arXiv:2404.07965**Zhenghao Lin, Zhibin Gou, Yeyun Gong, et al.*2024 · 59 citations*](https://md.research.elamur.ai/free/en/papers/lin-2024-rho-1-all-tokens-need/) 3. [**Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws arXiv:2401.00448**Nikhil Sardana, Jacob Portes, Sasha Doubov, et al.*2024 · 57 citations*](https://md.research.elamur.ai/free/en/papers/sardana-2024-chinchilla-optimal-accounting-inference-language/) 4. [**RegMix: Data Mixture as Regression for Language Model Pre-training arXiv:2407.01492**Qian Liu, Xiaosen Zheng, Niklas Muennighoff, et al.*2024 · 57 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-regmix-data-mixture-regression-language/) 5. [**Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies arXiv:2407.13623**Chaofan Tao, Qian Liu, Longxu Dou, et al.*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/tao-2024-scaling-laws-vocabulary-larger-models/) 6. **[Paloma: A Benchmark for Evaluating Language Model Fit](https://md.research.elamur.ai/free/en/papers/magnusson-2023-paloma-benchmark-evaluating-language-model/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Ian Magnusson, Akshita Bhagia, Valentin Hofmann, et al.*2023 · 42 citations* 7. [**Observational Scaling Laws and the Predictability of Language Model Performance arXiv:2405.10938**Yangjun Ruan, Chris J. Maddison, Tatsunori Hashimoto*2024 · 40 citations*](https://md.research.elamur.ai/free/en/papers/ruan-2024-observational-scaling-laws-predictability-language/) 8. **[Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations](https://md.research.elamur.ai/free/en/papers/hagele-2024-scaling-laws-compute-optimal-training/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Alexander Hägele, Elie Bakouch, Atli Kosson, et al.*2024 · 31 citations* 9. [**Distillation Scaling Laws arXiv:2502.08606**Dan Busbridge, Amitis Shidani, Floris Weers, et al.*2025 · 26 citations*](https://md.research.elamur.ai/free/en/papers/busbridge-2025-distillation-scaling-laws/) 10. **[Resolving Discrepancies in Compute-Optimal Scaling of Language Models](https://md.research.elamur.ai/free/en/papers/porian-2024-resolving-discrepancies-compute-optimal-scaling/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Tomer Porian, Mitchell Wortsman, Jenia Jitsev, et al.*2024 · 19 citations* 11. [**Bilingual Adaptation of Monolingual Foundation Models arXiv:2407.12869**Gurpreet Gosal, Yishi Xu, Gokul Ramakrishnan, et al.*2024 · 7 citations*](https://md.research.elamur.ai/free/en/papers/gosal-2024-bilingual-adaptation-monolingual-foundation-models/) 12. **[Rethinking Data Mixture for Large Language Models: A Comprehensive Survey and New Perspectives](https://md.research.elamur.ai/free/en/papers/liu-2026-rethinking-data-mixture-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yajiao Liu, Congliang Chen, Junchi Yang, et al.*2026* 13. **[Scaling Law for Multimodal Large Language Model Supervised Fine-Tuning](https://md.research.elamur.ai/free/en/papers/zhang-2026-scaling-law-multimodal-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**YiFan Zhang, Tao Yu, Feng Li, et al.*2026* 14. **[UniGeM: Unifying Data Selection and Mixing via Geometric Exploration and Mining](https://md.research.elamur.ai/free/en/papers/wang-2026-unigem-unifying-data-selection-mixing/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Changhao Wang, Yunfeiyu, Xinhao Yao, et al.*2026* 28 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/gadre-2024-language-models-scale-reliably-over/) — Language models scale reliably with over-training and on downstream tasks