🔗 Who cites «Physics of Language Models: Part 2.2, How to Learn From Mistakes on Grade-School Math Problems»

27 papers of our library cite it (of the 149,439 whose bibliographies we read) · 17 with a page here, the most cited first · ← back to «Physics of Language Models: Part 2.2, How to Learn From Mistakes on Grade-School Math Problems»

1. Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language ModelsScience China Information SciencesQiguang Chen, Libo Qin, Jinhao Liu, et al.2025 · 176 citations
2. Training Language Models to Self-Correct via Reinforcement LearningarXiv:2409.12917Aviral Kumar, Vincent Zhuang, Rishabh Agarwal, et al.2024 · 159 citations
3. Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRsarXiv:2503.01307Kanishk Gandhi, Ayush Chakravarthy, Anikait Singh, et al.2025 · 123 citations
4. Physics of Language Models: Part 3.1, Knowledge Storage and ExtractionarXiv:2309.14316Zeyuan Allen-Zhu, Yuanzhi Li2023 · 116 citations
5. O1 Replication Journey: A Strategic Progress Report — Part 1arXiv:2410.18982Yiwei Qin, Xuefeng Li, Haoyang Zou, et al.2024 · 86 citations
6. WebThinker: Empowering Large Reasoning Models with Deep Research CapabilityConference on Neural Information Processing Systems (NeurIPS)Xiaoxi Li, Jiajie Jin, Guanting Dong, et al.2025 · 72 citations
7. Physics of Language Models: Part 3.2, Knowledge ManipulationarXiv:2309.14402Zeyuan Allen-Zhu, Yuanzhi Li2023 · 72 citations
8. Physics of Language Models: Part 2.1, Grade-School Math and the Hidden Reasoning ProcessarXiv:2407.20311Tian Ye, Zicheng Xu, Yuanzhi Li, et al.2024 · 58 citations
9. Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-ThoughtarXiv:2501.04682Violet Xiang, Charlie Snell, Kanishk Gandhi, et al.2025 · 47 citations
10. Physics of Language Models: Part 1, Learning Hierarchical Language StructuresarXiv:2305.13673Zeyuan Allen-Zhu, Yuanzhi Li2023 · 42 citations
11. Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning PerspectivearXiv:2412.14135Zhiyuan Zeng, Qinyuan Cheng, Zhangyue Yin, et al.2024 · 33 citations
12. A Survey on Large Language Models for Mathematical ReasoningACM Computing SurveysPeng-Yuan Wang, Tian-Shuo Liu, Chenyang Wang, et al.2025 · 26 citations
13. GSM-Infinite: How Do Your LLMs Behave over Infinitely Increasing Context Length and Reasoning Complexity?arXiv:2502.05252Yang Zhou, Hongyi Liu, Zhuoming Chen, et al.2025 · 21 citations
14. Causal Language Modeling Can Elicit Search and Reasoning Capabilities on Logic PuzzlesConference on Neural Information Processing Systems (NeurIPS)Kulin Shah, Nishanth Dikkala, Xin Wang, et al.2024 · 12 citations
15. Advancing Mathematical Reasoning in Language Models: The Impact of Problem-Solving Data, Data Synthesis Methods, and Training StagesarXiv:2501.14002Zui Chen, Tianqiao Liu, Mi Tian, et al.2025 · 9 citations
16. Streaming Hallucination Detection in Long Chain-of-Thought ReasoningFindings of the Association for Computational LinguisticsHaolang Lu, Minghui Pan, Ripeng LI, et al.2026 · 1 citation
17. Tailored Primitive Initialization is the Secret Key to Reinforcement LearningAnnual Meeting of the Association for Computational Linguistics (ACL)Yihang Yao, Guangtao Zeng, Raina Wu, et al.2026

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