--- title: "Who cites Reinforcement Learning for — 37 citing papers" description: "37 papers of the Elamur Research library cite «Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn't», found…" canonical: "https://research.elamur.ai/free/en/papers/dang-2025-reinforcement-learning-reasoning-small-llms/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/dang-2025-reinforcement-learning-reasoning-small-llms/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Reinforcement Learning for Reasoning in Small…](https://md.research.elamur.ai/free/en/papers/dang-2025-reinforcement-learning-reasoning-small-llms/) / cited by # Who cites «Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn't» 37 papers of our library cite it (of the 149,439 whose bibliographies we read) · 14 with a page here, the most cited first · [← back to «Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn't»](https://md.research.elamur.ai/free/en/papers/dang-2025-reinforcement-learning-reasoning-small-llms/) 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. [**ToolRL: Reward is All Tool Learning Needs arXiv:2504.13958**Cheng Qian, Emre Can Acikgoz, Qi He, et al.*2025 · 94 citations*](https://md.research.elamur.ai/free/en/papers/qian-2025-toolrl-reward-all-tool-learning/) 3. [**DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning arXiv:2504.11456**Zhiwei He, Tian Liang, Jiahao Xu, et al.*2025 · 78 citations*](https://md.research.elamur.ai/free/en/papers/he-2025-deepmath-103k-large-scale-challenging/) 4. [**Phi-4-reasoning Technical Report arXiv:2504.21318**Marah Abdin, Sahaj Agarwal, Ahmed Awadallah, et al.*2025 · 64 citations*](https://md.research.elamur.ai/free/en/papers/abdin-2025-phi-4-reasoning-technical-report/) 5. [**Efficient Reasoning Models: A Survey arXiv:2504.10903**Sicheng Feng, Gongfan Fang, Xinyin Ma, et al.*2025 · 37 citations*](https://md.research.elamur.ai/free/en/papers/feng-2025-efficient-reasoning-models-survey/) 6. [**A Sober Look at Progress in Language Model Reasoning: Pitfalls and Paths to Reproducibility arXiv:2504.07086**Andreas Hochlehnert, Hardik Bhatnagar, Vishaal Udandarao, et al.*2025 · 36 citations*](https://md.research.elamur.ai/free/en/papers/hochlehnert-2025-sober-look-progress-language-model/) 7. **[CompassVerifier: A Unified and Robust Verifier for LLMs Evaluation and Outcome Reward](https://md.research.elamur.ai/free/en/papers/liu-2025-compassverifier-unified-robust-verifier-llms/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Shudong Liu, Hongwei Liu, Junnan Liu, et al.*2025 · 16 citations* 8. **[Arctic-Text2SQL-R1: Simple Rewards, Strong Reasoning in Text-to-SQL](https://md.research.elamur.ai/free/en/papers/yao-2026-arctic-text2sql-r1-simple-rewards/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhewei Yao, Guoheng Sun, Łukasz Borchmann, et al.*2026 · 15 citations* 9. [**RSafe: Incentivizing proactive reasoning to build robust and adaptive LLM safeguards arXiv:2506.07736 · 39th Conference on Neural Information Processing Systems (NeurIPS 2025). 39th Conference on Neural Information Processing Systems (NeurIPS 2025). 39th Conference on Neural Information Processing Systems (NeurIPS 2025)**Jingnan Zheng, Xiangtian Ji, Yijun Lu, et al.*2025 · 5 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2025-rsafe-incentivizing-proactive-reasoning-build/) 10. **[Logit Arithmetic Elicits Long Reasoning Capabilities Without Training](https://md.research.elamur.ai/free/en/papers/zhang-2026-logit-arithmetic-elicits-long-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yunxiang Zhang, Muhammad Khalifa, Lechen Zhang, et al.*2026 · 2 citations* 11. **[AAPO: Enhancing the Reasoning Capabilities of LLMs with Advantage Margin](https://md.research.elamur.ai/free/en/papers/xiong-2026-aapo-enhancing-reasoning-capabilities-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jian Xiong, Jingbo Zhou, Jingyong Ye, et al.*2026* 12. **[Reinforcement Learning for Latent-Space Thinking in LLMs](https://md.research.elamur.ai/free/en/papers/ozeren-2026-reinforcement-learning-latent-space-thinking/) [Edition of the Swiss Text Analytics Conference](https://md.research.elamur.ai/free/en/venues/swisstext/)**Enes Özeren, Matthias Assenmacher*2026* 13. **[Taming Extreme Tokens: Covariance-Aware GRPO with Gaussian-Kernel Advantage Reweighting](https://md.research.elamur.ai/free/en/papers/wang-2026-taming-extreme-tokens-covariance-aware/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Cheng Wang, Qin Liu, Wenxuan Zhou, et al.*2026* 14. **[TrigReason: Trigger-Based Collaboration between Small and Large Reasoning Models](https://md.research.elamur.ai/free/en/papers/zhao-2026-trigreason-trigger-based-collaboration-between/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yi Zhao, Yajuan Peng, Cam-Tu Nguyen, et al.*2026* 23 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/dang-2025-reinforcement-learning-reasoning-small-llms/) — Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn't