--- title: "Who cites ProRL: Prolonged Reinforcement — 57 citing papers" description: "57 papers of the Elamur Research library cite «ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large…" canonical: "https://research.elamur.ai/free/en/papers/liu-2025-prorl-prolonged-reinforcement-learning-expands/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/liu-2025-prorl-prolonged-reinforcement-learning-expands/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [ProRL: Prolonged Reinforcement Learning Expands…](https://md.research.elamur.ai/free/en/papers/liu-2025-prorl-prolonged-reinforcement-learning-expands/) / cited by # Who cites «ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models» 57 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 «ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models»](https://md.research.elamur.ai/free/en/papers/liu-2025-prorl-prolonged-reinforcement-learning-expands/) 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. [**Olmo 3 arXiv:2512.13961**Team Olmo: Allyson Ettinger, Amanda Bertsch, Bailey Kuehl, et al.*2025 · 130 citations*](https://md.research.elamur.ai/free/en/papers/ettinger-2025-olmo-3/) 3. **[Training Language Models to Reason Efficiently](https://md.research.elamur.ai/free/en/papers/arora-2025-training-language-models-reason-efficiently/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Daman Arora, Andrea Zanette*2025 · 74 citations* 4. **[Rethinking Entropy Interventions in RLVR: An Entropy Change Perspective](https://md.research.elamur.ai/free/en/papers/hao-2026-rethinking-entropy-interventions-rlvr-entropy/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhezheng Hao, Hong Wang, Haoyang Liu, et al.*2026 · 22 citations* 5. [**AceReason-Nemotron 1.1: Advancing Math and Code Reasoning through SFT and RL Synergy arXiv:2506.13284**Zihan Liu, Zhuolin Yang, Yang Chen, et al.*2025 · 21 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-acereason-nemotron-1-1-advancing/) 6. **[CriticLean: Critic-Guided Reinforcement Learning for Mathematical Formalization](https://md.research.elamur.ai/free/en/papers/peng-2026-criticlean-critic-guided-reinforcement-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhongyuan Peng, Yifan Yao, Kaijing Ma, et al.*2026 · 12 citations* 7. [**ERNIE 5.0 Technical Report arXiv:2602.04705**Haifeng Wang, Hua Wu, Tian Wu, et al.*2026 · 7 citations*](https://md.research.elamur.ai/free/en/papers/wang-2026-ernie-5-0-technical/) 8. **[Scaling Behaviors of LLM Reinforcement Learning Post-Training: An Empirical Study in Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/tan-2026-scaling-behaviors-llm-reinforcement-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zelin Tan, Hejia Geng, Xiaohang Yu, et al.*2026 · 6 citations* 9. [**rStar2-Agent: Agentic Reasoning Technical Report arXiv:2508.20722**Ning Shang, Yifei Liu, Yi Zhu, et al.*2025 · 6 citations*](https://md.research.elamur.ai/free/en/papers/shang-2025-rstar2-agent-agentic-reasoning/) 10. **[Learning to Optimize Multi-Objective Alignment Through Dynamic Reward Weighting](https://md.research.elamur.ai/free/en/papers/lu-2026-learning-optimize-multi-objective-alignment/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Yining Lu, Zilong Wang, Shiyang Li, et al.*2026 · 4 citations* 11. **[Rethinking Sample Polarity in Reinforcement Learning with Verifiable Rewards](https://md.research.elamur.ai/free/en/papers/tang-2026-rethinking-sample-polarity-reinforcement-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xinyu Tang, Yuliang Zhan, Zhixun Li, et al.*2026 · 4 citations* 12. **[Gained in Translation: Privileged Pairwise Judges Enhance Multilingual Reasoning](https://md.research.elamur.ai/free/en/papers/sutawika-2026-gained-translation-privileged-pairwise-judges/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lintang Sutawika, Gokul Swamy, Steven Wu, et al.*2026 · 2 citations* 13. **[Entropy Ratio Clipping as a Soft Global Constraint for Stable Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/su-2026-entropy-ratio-clipping-as-soft/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhenpeng Su, Leiyu Pan, Minxuan Lv, et al.*2026 · 1 citation* 14. **[Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models](https://md.research.elamur.ai/free/en/papers/phan-2026-beyond-reasoning-gains-mitigating-general/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hoang Phan, Xianjun Yang, Yuanshun Yao, et al.*2026* 15. **[Is Micro Domain-Adaptive Pre-Training Effective for Real-World Operations? Multi-Step Evaluation Reveals Potential and Bottlenecks](https://md.research.elamur.ai/free/en/papers/tsunokake-2026-is-micro-domain-adaptive-pre/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Masaya Tsunokake, Yuta Koreeda, Terufumi Morishita, et al.*2026* 16. **[Memorization, Emergence, and Explaining Reversal Failures: A Controlled Study of Relational Semantics in LLMs](https://md.research.elamur.ai/free/en/papers/zhu-2026-memorization-emergence-explaining-reversal-failures/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yihua Zhu, Qianying Liu, Jiaxin Wang, et al.*2026* 17. **[Revisiting Entropy Regularization: Adaptive Coefficient Unlocks Its Potential for LLM Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/zhang-2026-revisiting-entropy-regularization-adaptive-coefficient/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiaoyun Zhang, Xiaojian Yuan, Di Huang, et al.*2026* 18. **[ScaleBox: Enabling High-Fidelity and Scalable Code Verification for Large Language Models](https://md.research.elamur.ai/free/en/papers/zheng-2026-scalebox-enabling-high-fidelity-scalable/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiasheng Zheng, Xin Zheng, Boxi Cao, et al.*2026* 19. **[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* 20. **[Teaching Language Models to Forecast Research Success Through Comparative Idea Evaluation](https://md.research.elamur.ai/free/en/papers/mule-2026-teaching-language-models-forecast-research/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Srujan P Mule, Aniketh Garikaparthi, Manasi Patwardhan*2026* 21. **[Understanding and Preventing Entropy Collapse in RLVR with On-Policy Entropy Flow Optimization](https://md.research.elamur.ai/free/en/papers/xu-2026-understanding-preventing-entropy-collapse-rlvr/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Huimin Xu, Shuai Zhao, Xiaobao Wu, et al.*2026* 36 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/liu-2025-prorl-prolonged-reinforcement-learning-expands/) — ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models