🔗 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»

1. DataComp-LM: In search of the next generation of training sets for language modelsConference on Neural Information Processing Systems (NeurIPS)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 TrustworthinessACM Transactions on Intelligent Systems and TechnologyFali Wang, Zhiwei Zhang, Xianren Zhang, et al.2024 · 159 citations
3. Process Reinforcement through Implicit RewardsarXiv:2502.01456Ganqu Cui, Lifan Yuan, Zefan Wang, et al.2025 · 151 citations
4. OpenThoughts: Data Recipes for Reasoning ModelsarXiv:2506.04178Etash Guha, Ryan Marten, Sedrick Keh, et al.2025 · 122 citations
5. Better & Faster Large Language Models via Multi-token PredictionarXiv:2404.19737Fabian Gloeckle, Badr Youbi Idrissi, Baptiste Roziere, et al.2024 · 90 citations
6. MedXpertQA: Benchmarking Expert-Level Medical Reasoning and UnderstandingarXiv:2501.18362Yuxin Zuo, Shang Qu, Yifei Li, et al.2025 · 65 citations
7. VinePPO: Refining Credit Assignment in RL Training of LLMsarXiv:2410.01679Amirhossein Kazemnejad, Milad Aghajohari, Eva Portelance, et al.2024 · 63 citations
8. RegMix: Data Mixture as Regression for Language Model Pre-trainingarXiv:2407.01492Qian Liu, Xiaosen Zheng, Niklas Muennighoff, et al.2024 · 57 citations
9. Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language ModelsarXiv:2407.07263Jupinder Parmar, Sanjev Satheesh, Mostofa Patwary, et al.2024 · 36 citations
10. EMO: Embedding Model Distillation via Intra-Model Relation and Optimal Transport AlignmentsConference on Empirical Methods in Natural Language Processing (EMNLP)Minh-Phuc Truong, Hai An Vu, Tu Vu, et al.2025 · 10 citations
11. Selective Preference Optimization via Token-Level Reward Function EstimationConference on Empirical Methods in Natural Language Processing (EMNLP)Kailai Yang, Zhiwei Liu, Qianqian Xie, et al.2025 · 8 citations
12. ProFit: Leveraging High-Value Signals in SFT via Probability-Guided Token SelectionFindings of the Association for Computational LinguisticsTao Liu, Taiqiang Wu, Runming Yang, et al.2026 · 4 citations
13. LightReasoner: Can Small Language Models Teach Large Language Models Reasoning?Annual Meeting of the Association for Computational Linguistics (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 LearningAnnual Meeting of the Association for Computational Linguistics (ACL)Hanbing Liu, Lang Cao, Yuanyi Ren, et al.2026 · 1 citation
15. The Bidirectional Process Reward ModelAnnual Meeting of the Association for Computational Linguistics (ACL)Lingyin Zhang, Jun Gao, Xiaoxue Ren, et al.2026 · 1 citation
16. VocabTailor: Dynamic Vocabulary Selection for Downstream Tasks in Small Language ModelsFindings of the Association for Computational LinguisticsHanling Zhang, Yayu Zhou, Tongcheng Fang, et al.2026 · 1 citation
17. Balancing Knowledge Breadth and Task Depth for Effective Domain Adaptation Fine-TuningFindings of the Association for Computational LinguisticsMu Zhang, Yuxiang Chu, Guangya Yu, et al.2026
18. TiKMiX: Efficient Semi-Dynamic Data Mixture via Data Influence for LLM Pre-trainingAnnual Meeting of the Association for Computational Linguistics (ACL)Yifan Wang, Binbinliu, Fengze Liu, et al.2026

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