--- title: "Who cites Learning to Reason under Off-Policy Guidance" description: "61 papers of the Elamur Research library cite «Learning to Reason under Off-Policy Guidance», found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/papers/yan-2025-learning-reason-under-off-policy/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/yan-2025-learning-reason-under-off-policy/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Learning to Reason under Off-Policy Guidance](https://md.research.elamur.ai/free/en/papers/yan-2025-learning-reason-under-off-policy/) / cited by # Who cites «Learning to Reason under Off-Policy Guidance» 61 papers of our library cite it (of the 149,439 whose bibliographies we read) · 19 with a page here, the most cited first · [← back to «Learning to Reason under Off-Policy Guidance»](https://md.research.elamur.ai/free/en/papers/yan-2025-learning-reason-under-off-policy/) 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. [**The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models arXiv:2505.22617**Ganqu Cui, Yuchen Zhang, Jiacheng Chen, et al.*2025 · 101 citations*](https://md.research.elamur.ai/free/en/papers/cui-2025-entropy-mechanism-reinforcement-learning-reasoning/) 3. **[StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason](https://md.research.elamur.ai/free/en/papers/zhang-2026-stephint-multi-level-stepwise-hints/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kaiyi Zhang, Ang Lv, Jinpeng Li, et al.*2026 · 13 citations* 4. **[Scaling Reasoning, Losing Control: Evaluating Instruction Following in Large Reasoning Models](https://md.research.elamur.ai/free/en/papers/fu-2026-scaling-reasoning-losing-control-evaluating/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tingchen Fu, Yafu Li, Jiawei Gu, et al.*2026 · 12 citations* 5. **[Lost in Literalism: How Supervised Training Shapes Translationese in LLMs](https://md.research.elamur.ai/free/en/papers/li-2025-lost-literalism-how-supervised-training/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yafu Li, Ronghao Zhang, Zhilin Wang, et al.*2025 · 12 citations* 6. **[ATLAS: Orchestrating Heterogeneous Models and Tools for Multi-Domain Complex Reasoning](https://md.research.elamur.ai/free/en/papers/wu-2026-atlas-orchestrating-heterogeneous-models-tools/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jinyang Wu, Guocheng Zhai, Ruihan Jin, et al.*2026 · 8 citations* 7. **[RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization](https://md.research.elamur.ai/free/en/papers/dong-2026-rl-plus-countering-capability-boundary/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yihong Dong, Xue Jiang, Yongding Tao, et al.*2026 · 6 citations* 8. **[Do Not Step Into the Same River Twice: Learning to Reason from Trial and Error](https://md.research.elamur.ai/free/en/papers/tang-2026-do-not-step-into-same/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chenming Tang, Hsiu-Yuan Huang, Weijie Liu, et al.*2026 · 3 citations* 9. **[Harnessing Negative Signals: Reinforcement Distillation from Teacher Data for LLM Reasoning](https://md.research.elamur.ai/free/en/papers/xu-2026-harnessing-negative-signals-reinforcement-distillation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shuyao Xu, Cheng Peng, Jiangxuan Long, et al.*2026 · 3 citations* 10. **[Learning While Staying Curious: Entropy-Preserving Supervised Fine-Tuning via Adaptive Self-Distillation for Large Reasoning Models](https://md.research.elamur.ai/free/en/papers/wang-2026-learning-while-staying-curious-entropy/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hao Wang, Hao Gu, Hongming Piao, et al.*2026 · 3 citations* 11. **[No More Stale Feedback: Co-Evolving Critics for Open-World Agent Learning](https://md.research.elamur.ai/free/en/papers/li-2026-no-more-stale-feedback-co/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhicong Li, Lingjie Jiang, Yulan Hu, et al.*2026 · 3 citations* 12. **[From Off-Policy to On-Policy: Enhancing GUI Agents via Bi-level Expert-to-Policy Assimilation](https://md.research.elamur.ai/free/en/papers/wang-2026-off-policy-policy-enhancing-gui/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zezhou Wang, Ziyun Zhang, Xiaoyi Zhang, et al.*2026 · 1 citation* 13. **[Don't Tell the Answer, Truly Guide the Reasoning During RL Rollouts](https://md.research.elamur.ai/free/en/papers/wang-2026-don-t-tell-answer-truly/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xinyi Wang, Jinyi Han, Zishang Jiang, et al.*2026* 14. **[Exploration-Exploitation Reshaping towards Efficient Reasoning for Large Language Models](https://md.research.elamur.ai/free/en/papers/shi-2026-exploration-exploitation-reshaping-efficient-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yufeng Shi, Weilin Luo, Yuxiang Zhang, et al.*2026* 15. **[FeedTrans: Enhance Machine Translation via Feedback](https://md.research.elamur.ai/free/en/papers/sun-2026-feedtrans-enhance-machine-translation-feedback/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Zengkui Sun, Jiali Zeng, Jiaan Wang, et al.*2026* 16. **[Mixed-Policy GRPO for Text-to-SQL with Off-Policy Data Generation](https://md.research.elamur.ai/free/en/papers/sterbentz-2026-mixed-policy-grpo-text-sql/) [Workshop on Structured Understanding, Retrieval, and Generation in the LLM Era](https://md.research.elamur.ai/free/en/venues/surgellm/)**Marko Sterbentz, Michael Glass, Nhan H Pham, et al.*2026* 17. **[Reinforcing Agentic Search Via Reward Density Optimization](https://md.research.elamur.ai/free/en/papers/luo-2026-reinforcing-agentic-search-reward-density/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kun Luo, Hongjin Qian, Zheng Liu, et al.*2026* 18. **[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* 19. **[Triviality Corrected Endogenous Reward](https://md.research.elamur.ai/free/en/papers/wang-2026-triviality-corrected-endogenous-reward/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xinda Wang, Zhengxu Hou, Yangshijie Zhang, et al.*2026* 42 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/yan-2025-learning-reason-under-off-policy/) — Learning to Reason under Off-Policy Guidance