--- title: "Who cites On Memorization of Large — 42 citing papers" description: "42 papers of the Elamur Research library cite «On Memorization of Large Language Models in Logical Reasoning», found in their…" canonical: "https://research.elamur.ai/free/en/papers/xie-2025-memorization-large-language-models-logical/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/xie-2025-memorization-large-language-models-logical/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [On Memorization of Large Language Models in…](https://md.research.elamur.ai/free/en/papers/xie-2025-memorization-large-language-models-logical/) / cited by # Who cites «On Memorization of Large Language Models in Logical Reasoning» 42 papers of our library cite it (of the 149,439 whose bibliographies we read) · 10 with a page here, the most cited first · [← back to «On Memorization of Large Language Models in Logical Reasoning»](https://md.research.elamur.ai/free/en/papers/xie-2025-memorization-large-language-models-logical/) 1. **[Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-towards-reasoning-era-survey-long/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Libo Qin, Jinhao Liu, et al.*2025 · 176 citations* 2. [**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/) 3. [**REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization arXiv:2501.03262**Jian Hu, Jason Klein Liu, Haotian Xu, et al.*2025 · 104 citations*](https://md.research.elamur.ai/free/en/papers/hu-2025-reinforce-stabilizing-critic-free-policy/) 4. [**ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning arXiv:2502.01100**Bill Yuchen Lin, Ronan Le Bras, Kyle Richardson, et al.*2025 · 60 citations*](https://md.research.elamur.ai/free/en/papers/lin-2025-zebralogic-scaling-limits-llms-logical/) 5. **[Language Mixing in Reasoning Language Models: Patterns, Impact, and Internal Causes](https://md.research.elamur.ai/free/en/papers/wang-2025-language-mixing-reasoning-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Mingyang Wang, Lukas Lange, Heike Adel, et al.*2025 · 16 citations* 6. **[Beyond Math: Stories as a Testbed for Memorization-Constrained Reasoning in LLMs](https://md.research.elamur.ai/free/en/papers/jiang-2026-beyond-math-stories-as-testbed/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Yuxuan Jiang, Francis Ferraro*2026 · 10 citations* 7. **[Learning to Refine: Self-Refinement of Parallel Reasoning in LLMs](https://md.research.elamur.ai/free/en/papers/wang-2026-learning-refine-self-refinement-parallel/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Qibin Wang, Pu Zhao, Shaohan Huang, et al.*2026* 8. **[SATQuest: A Verifier for Logical Reasoning Evaluation and Reinforcement Fine-Tuning of LLMs](https://md.research.elamur.ai/free/en/papers/zhao-2026-satquest-verifier-logical-reasoning-evaluation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yanxiao Zhao, Yaqian Li, Zihao Bo, et al.*2026* 9. **[Shorten After You're Right: Lazy Length Penalties for Reasoning RL](https://md.research.elamur.ai/free/en/papers/yuan-2026-shorten-after-you-re-right/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Danlong Yuan, Tian Xie, Shaohan Huang, et al.*2026* 10. **[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* 32 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/xie-2025-memorization-large-language-models-logical/) — On Memorization of Large Language Models in Logical Reasoning