--- title: "Who cites Data Mixing Laws: Optimizing — 50 citing papers" description: "50 papers of the Elamur Research library cite «Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance»…" canonical: "https://research.elamur.ai/free/en/papers/ye-2024-data-mixing-laws-optimizing-data/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/ye-2024-data-mixing-laws-optimizing-data/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Data Mixing Laws: Optimizing Data Mixtures by…](https://md.research.elamur.ai/free/en/papers/ye-2024-data-mixing-laws-optimizing-data/) / cited by # Who cites «Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance» 50 papers of our library cite it (of the 149,439 whose bibliographies we read) · 15 with a page here, the most cited first · [← back to «Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance»](https://md.research.elamur.ai/free/en/papers/ye-2024-data-mixing-laws-optimizing-data/) 1. [**MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies arXiv:2404.06395**Shengding Hu, Yuge Tu, Xu Han, et al.*2024 · 269 citations*](https://md.research.elamur.ai/free/en/papers/hu-2024-minicpm-unveiling-potential-small-language/) 2. [**Olmo 3 arXiv:2512.13961**Team Olmo: Allyson Ettinger, Amanda Bertsch, Bailey Kuehl, et al.*2025 · 130 citations*](https://md.research.elamur.ai/free/en/papers/ettinger-2025-olmo-3/) 3. [**A Survey on Data Selection for Language Models arXiv:2402.16827**Alon Albalak, Yanai Elazar, Sang Michael Xie, et al.*2024 · 95 citations*](https://md.research.elamur.ai/free/en/papers/albalak-2024-survey-data-selection-language-models/) 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. **[DDK: Distilling Domain Knowledge for Efficient Large Language Models](https://md.research.elamur.ai/free/en/papers/liu-2024-ddk-distilling-domain-knowledge-efficient/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jiaheng Liu, Chenchen Zhang, Jinyang Guo, et al.*2024 · 23 citations* 6. [**Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models arXiv:2412.02980**Alex Havrilla, Andrew Dai, Laura O'Mahony, et al.*2024 · 17 citations*](https://md.research.elamur.ai/free/en/papers/havrilla-2024-surveying-effects-quality-diversity-complexity/) 7. [**MiniPLM: Knowledge Distillation for Pre-Training Language Models arXiv:2410.17215**Yuxian Gu, Hao Zhou, Fandong Meng, et al.*2024 · 12 citations*](https://md.research.elamur.ai/free/en/papers/gu-2024-miniplm-knowledge-distillation-pre-training/) 8. **[Data Mixing Agent: Learning to Re-weight Domains for Continual Pre-training](https://md.research.elamur.ai/free/en/papers/yang-2026-data-mixing-agent-learning-re/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kailai Yang, Xiao Liu, Lei Ji, et al.*2026 · 1 citation* 9. **[DaMo: Data Mixing Optimizer in Fine-tuning Multimodal LLMs for Mobile Phone Agents](https://md.research.elamur.ai/free/en/papers/shi-2026-damo-data-mixing-optimizer-fine/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kai Shi, Jun Yang, Ni Yang, et al.*2026* 10. **[Data Foundations of Long-Context Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/sun-2026-data-foundations-long-context-language/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Zechen Sun, Yuyang Sun, Zhaochen Su, et al.*2026* 11. **[Rethinking Data Mixing from the Perspective of Large Language Models](https://md.research.elamur.ai/free/en/papers/xu-2026-rethinking-data-mixing-perspective-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuanjian Xu, Tianze Sun, Changwei Xu, et al.*2026* 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. **[TiKMiX: Efficient Semi-Dynamic Data Mixture via Data Influence for LLM Pre-training](https://md.research.elamur.ai/free/en/papers/wang-2026-tikmix-efficient-semi-dynamic-data/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yifan Wang, Binbinliu, Fengze Liu, et al.*2026* 14. **[TReX: Tokenizer Regression for Optimal Data Mixture](https://md.research.elamur.ai/free/en/papers/won-2026-trex-tokenizer-regression-optimal-data/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Inho Won, Hangyeol Yoo, Minkyung Cho, et al.*2026* 15. [**360Zhinao Technical Report arXiv:2405.13386***2024*](https://md.research.elamur.ai/free/en/papers/360zhinao-team-2024-360zhinao-technical-report/) 35 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/ye-2024-data-mixing-laws-optimizing-data/) — Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance