---
title: "Who cites Physics of Language Models: Part — 52 citing papers"
description: "52 papers of the Elamur Research library cite «Physics of Language Models: Part 2.1, Grade-School Math and the Hidden Reasoning…"
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---
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# Who cites «Physics of Language Models: Part 2.1, Grade-School Math and the Hidden Reasoning Process»

52 papers of our library cite it (of the 149,439 whose bibliographies we read) · 21 with a page here, the most cited first · [← back to «Physics of Language Models: Part 2.1, Grade-School Math and the Hidden Reasoning Process»](https://md.research.elamur.ai/free/en/papers/ye-2024-physics-language-models-part-2/)

1. [**GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models arXiv:2410.05229**Iman Mirzadeh, Keivan Alizadeh, Hooman Shahrokhi, et al.*2024 · 273 citations*](https://md.research.elamur.ai/free/en/papers/mirzadeh-2024-gsm-symbolic-understanding-limitations-mathematical/)
2. **[Large Language Diffusion Models](https://md.research.elamur.ai/free/en/papers/nie-2025-large-language-diffusion-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Shen Nie, Fengqi Zhu, Zebin You, et al.*2025 · 178 citations*
3. [**SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training arXiv:2501.17161**Tianzhe Chu, Yuexiang Zhai, Jihan Yang, et al.*2025 · 160 citations*](https://md.research.elamur.ai/free/en/papers/chu-2025-sft-memorizes-rl-generalizes-comparative/)
4. [**Physics of Language Models: Part 3.1, Knowledge Storage and Extraction arXiv:2309.14316**Zeyuan Allen-Zhu, Yuanzhi Li*2023 · 116 citations*](https://md.research.elamur.ai/free/en/papers/allen-zhu-2023-physics-language-models-part-3/)
5. [**Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning arXiv:2502.14768**Tian Xie, Zitian Gao, Qingnan Ren, et al.*2025 · 104 citations*](https://md.research.elamur.ai/free/en/papers/xie-2025-logic-rl-unleashing-llm/)
6. [**When More is Less: Understanding Chain-of-Thought Length in LLMs arXiv:2502.07266**Yuyang Wu, Yifei Wang, Ziyu Ye, et al.*2025 · 73 citations*](https://md.research.elamur.ai/free/en/papers/wu-2025-more-less-understanding-chain-thought/)
7. [**Physics of Language Models: Part 3.2, Knowledge Manipulation arXiv:2309.14402**Zeyuan Allen-Zhu, Yuanzhi Li*2023 · 72 citations*](https://md.research.elamur.ai/free/en/papers/allen-zhu-2023-physics-language-models-part-3-2/)
8. **[On Memorization of Large Language Models in Logical Reasoning](https://md.research.elamur.ai/free/en/papers/xie-2025-memorization-large-language-models-logical/) [International Joint Conference on Natural Language Processing (IJCNLP)](https://md.research.elamur.ai/free/en/venues/ijcnlp/)**Chulin Xie, Yangsibo Huang, Chiyuan Zhang, et al.*2025 · 45 citations*
9. [**On the Diversity of Synthetic Data and its Impact on Training Large Language Models arXiv:2410.15226**Hao Chen, Abdul Waheed, Xiang Li, et al.*2024 · 35 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-diversity-synthetic-data-impact-training/)
10. [**How Is LLM Reasoning Distracted by Irrelevant Context? An Analysis Using a Controlled Benchmark**Minglai Yang, Ethan Huang, Liang Zhang, et al.*2025 · 25 citations*](https://md.research.elamur.ai/free/en/papers/yang-2025-llm-reasoning-distracted-irrelevant-context/)
11. [**Scaling Latent Reasoning via Looped Language Models arXiv:2510.25741**Rui-Jie Zhu, Zixuan Wang, Kai Hua, et al.*2025 · 22 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2025-scaling-latent-reasoning-looped-language/)
12. [**GSM-Infinite: How Do Your LLMs Behave over Infinitely Increasing Context Length and Reasoning Complexity? arXiv:2502.05252**Yang Zhou, Hongyi Liu, Zhuoming Chen, et al.*2025 · 21 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2025-gsm-infinite-llms-behave-over/)
13. [**Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models arXiv:2503.24377**Rui Wang, Hongru Wang, Boyang Xue, et al.*2025 · 16 citations*](https://md.research.elamur.ai/free/en/papers/wang-2025-harnessing-reasoning-economy-survey-efficient/)
14. **[What Happened in LLMs Layers when Trained for Fast vs. Slow Thinking: A Gradient Perspective](https://md.research.elamur.ai/free/en/papers/li-2025-what-happened-llms-layers-when/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ming Li, Yanhong Li, Tianyi Zhou*2025 · 15 citations*
15. [**Do LLMs Really Think Step-by-step In Implicit Reasoning? arXiv:2411.15862**Yijiong Yu*2024 · 10 citations*](https://md.research.elamur.ai/free/en/papers/yu-2024-llms-really-think-step-step/)
16. **[Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-locate-steer-improve-practical-survey/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hengyuan Zhang, Zhihao Zhang, Ercong Nie, et al.*2026 · 9 citations*
17. **[MTR-Bench: A Comprehensive Benchmark for Multi-Turn Reasoning Evaluation](https://md.research.elamur.ai/free/en/papers/li-2026-mtr-bench-comprehensive-benchmark-multi/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaoyuan Li, Keqin Bao, Yubo Ma, et al.*2026 · 9 citations*
18. [**Pangu Ultra: Pushing the Limits of Dense Large Language Models on Ascend NPUs arXiv:2504.07866**Yichun Yin, Wenyong Huang, Kaikai Song, et al.*2025 · 6 citations*](https://md.research.elamur.ai/free/en/papers/yin-2025-pangu-ultra-pushing-limits/)
19. **[Streaming Hallucination Detection in Long Chain-of-Thought Reasoning](https://md.research.elamur.ai/free/en/papers/lu-2026-streaming-hallucination-detection-long-chain/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Haolang Lu, Minghui Pan, Ripeng LI, et al.*2026 · 1 citation*
20. **[Curing Miracle Steps in LLM Mathematical Reasoning with Rubric Rewards](https://md.research.elamur.ai/free/en/papers/yuan-2026-curing-miracle-steps-llm-mathematical/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Youliang Yuan, Qiuyang Mang, Jingbang Chen, et al.*2026*
21. **[Tailored Primitive Initialization is the Secret Key to Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/yao-2026-tailored-primitive-initialization-is-secret/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yihang Yao, Guangtao Zeng, Raina Wu, et al.*2026*

31 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-physics-language-models-part-2/) — Physics of Language Models: Part 2.1, Grade-School Math and the Hidden Reasoning Process
