--- title: "Who cites Rho-1: Not All Tokens Are What — 57 citing papers" description: "57 papers of the Elamur Research library cite «Rho-1: Not All Tokens Are What You Need», found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/papers/lin-2024-rho-1-all-tokens-need/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/lin-2024-rho-1-all-tokens-need/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Rho-1: Not All Tokens Are What You Need](https://md.research.elamur.ai/free/en/papers/lin-2024-rho-1-all-tokens-need/) / cited by # Who cites «Rho-1: Not All Tokens Are What You Need» 57 papers of our library cite it (of the 149,439 whose bibliographies we read) · 18 with a page here, the most cited first · [← back to «Rho-1: Not All Tokens Are What You Need»](https://md.research.elamur.ai/free/en/papers/lin-2024-rho-1-all-tokens-need/) 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. **[A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness](https://md.research.elamur.ai/free/en/papers/wang-2024-comprehensive-survey-small-language-models/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Fali Wang, Zhiwei Zhang, Xianren Zhang, et al.*2024 · 159 citations* 3. [**Process Reinforcement through Implicit Rewards arXiv:2502.01456**Ganqu Cui, Lifan Yuan, Zefan Wang, et al.*2025 · 151 citations*](https://md.research.elamur.ai/free/en/papers/cui-2025-process-reinforcement-through-implicit-rewards/) 4. [**OpenThoughts: Data Recipes for Reasoning Models arXiv:2506.04178**Etash Guha, Ryan Marten, Sedrick Keh, et al.*2025 · 122 citations*](https://md.research.elamur.ai/free/en/papers/guha-2025-openthoughts-data-recipes-reasoning-models/) 5. [**Better & Faster Large Language Models via Multi-token Prediction arXiv:2404.19737**Fabian Gloeckle, Badr Youbi Idrissi, Baptiste Roziere, et al.*2024 · 90 citations*](https://md.research.elamur.ai/free/en/papers/gloeckle-2024-better-faster-large-language-models/) 6. [**MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding arXiv:2501.18362**Yuxin Zuo, Shang Qu, Yifei Li, et al.*2025 · 65 citations*](https://md.research.elamur.ai/free/en/papers/zuo-2025-medxpertqa-benchmarking-expert-level-medical/) 7. [**VinePPO: Refining Credit Assignment in RL Training of LLMs arXiv:2410.01679**Amirhossein Kazemnejad, Milad Aghajohari, Eva Portelance, et al.*2024 · 63 citations*](https://md.research.elamur.ai/free/en/papers/kazemnejad-2024-vineppo-refining-credit-assignment-rl/) 8. [**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/) 9. [**Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models arXiv:2407.07263**Jupinder Parmar, Sanjev Satheesh, Mostofa Patwary, et al.*2024 · 36 citations*](https://md.research.elamur.ai/free/en/papers/parmar-2024-reuse-don-t-retrain-recipe/) 10. **[EMO: Embedding Model Distillation via Intra-Model Relation and Optimal Transport Alignments](https://md.research.elamur.ai/free/en/papers/truong-2025-emo-embedding-model-distillation-intra/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Minh-Phuc Truong, Hai An Vu, Tu Vu, et al.*2025 · 10 citations* 11. **[Selective Preference Optimization via Token-Level Reward Function Estimation](https://md.research.elamur.ai/free/en/papers/yang-2025-selective-preference-optimization-token-level/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kailai Yang, Zhiwei Liu, Qianqian Xie, et al.*2025 · 8 citations* 12. **[ProFit: Leveraging High-Value Signals in SFT via Probability-Guided Token Selection](https://md.research.elamur.ai/free/en/papers/liu-2026-profit-leveraging-high-value-signals/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Tao Liu, Taiqiang Wu, Runming Yang, et al.*2026 · 4 citations* 13. **[LightReasoner: Can Small Language Models Teach Large Language Models Reasoning?](https://md.research.elamur.ai/free/en/papers/wang-2026-lightreasoner-can-small-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jingyuan Wang, Yankai Chen, Zhonghang Li, et al.*2026 · 1 citation* 14. **[Not All Tokens Matter: Towards Efficient LLM Reasoning via Token Significance in Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/liu-2026-not-all-tokens-matter-efficient/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hanbing Liu, Lang Cao, Yuanyi Ren, et al.*2026 · 1 citation* 15. **[The Bidirectional Process Reward Model](https://md.research.elamur.ai/free/en/papers/zhang-2026-bidirectional-process-reward-model/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lingyin Zhang, Jun Gao, Xiaoxue Ren, et al.*2026 · 1 citation* 16. **[VocabTailor: Dynamic Vocabulary Selection for Downstream Tasks in Small Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-vocabtailor-dynamic-vocabulary-selection-downstream/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hanling Zhang, Yayu Zhou, Tongcheng Fang, et al.*2026 · 1 citation* 17. **[Balancing Knowledge Breadth and Task Depth for Effective Domain Adaptation Fine-Tuning](https://md.research.elamur.ai/free/en/papers/zhang-2026-balancing-knowledge-breadth-task-depth/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mu Zhang, Yuxiang Chu, Guangya Yu, et al.*2026* 18. **[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* 39 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/lin-2024-rho-1-all-tokens-need/) — Rho-1: Not All Tokens Are What You Need