--- title: "Who cites OpenR: An Open Source — 48 citing papers" description: "48 papers of the Elamur Research library cite «OpenR: An Open Source Framework for Advanced Reasoning with Large Language Models»…" canonical: "https://research.elamur.ai/free/en/papers/wang-2024-openr-open-source-framework-advanced/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/wang-2024-openr-open-source-framework-advanced/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [OpenR: An Open Source Framework for Advanced…](https://md.research.elamur.ai/free/en/papers/wang-2024-openr-open-source-framework-advanced/) / cited by # Who cites «OpenR: An Open Source Framework for Advanced Reasoning with Large Language Models» 48 papers of our library cite it (of the 149,439 whose bibliographies we read) · 20 with a page here, the most cited first · [← back to «OpenR: An Open Source Framework for Advanced Reasoning with Large Language Models»](https://md.research.elamur.ai/free/en/papers/wang-2024-openr-open-source-framework-advanced/) 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. [**Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models arXiv:2501.09686**Fengli Xu, Qianyue Hao, Zefang Zong, et al.*2025 · 120 citations*](https://md.research.elamur.ai/free/en/papers/xu-2025-towards-large-reasoning-models-survey/) 3. [**HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs arXiv:2412.18925**Junying Chen, Zhenyang Cai, Ke Ji, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-huatuogpt-o1-towards-medical-complex/) 4. [**A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well? arXiv:2503.24235**Qiyuan Zhang, Fuyuan Lyu, Zexu Sun, et al.*2025 · 79 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2025-survey-test-time-scaling-large/) 5. [**Spurious Rewards: Rethinking Training Signals in RLVR arXiv:2506.10947**Rulin Shao, Shuyue Stella Li, Rui Xin, et al.*2025 · 53 citations*](https://md.research.elamur.ai/free/en/papers/shao-2025-spurious-rewards-rethinking-training-signals/) 6. [**Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling arXiv:2502.06703**Runze Liu, Junqi Gao, Jian Zhao, et al.*2025 · 41 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-1b-llm-surpass-405b-llm/) 7. **[Revisiting the Test-Time Scaling of o1-like Models: Do they Truly Possess Test-Time Scaling Capabilities?](https://md.research.elamur.ai/free/en/papers/zeng-2025-revisiting-test-time-scaling-o1/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhiyuan Zeng, Qinyuan Cheng, Zhangyue Yin, et al.*2025 · 38 citations* 8. [**Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective arXiv:2412.14135**Zhiyuan Zeng, Qinyuan Cheng, Zhangyue Yin, et al.*2024 · 33 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2024-scaling-search-learning-roadmap-reproduce/) 9. **[R-PRM: Reasoning-Driven Process Reward Modeling](https://md.research.elamur.ai/free/en/papers/she-2025-r-prm-reasoning-driven-process/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Shuaijie She, Junxiao Liu, Yifeng Liu, et al.*2025 · 18 citations* 10. [**Aviary: training language agents on challenging scientific tasks arXiv:2412.21154**Siddharth Narayanan, James D. Braza, Ryan-Rhys Griffiths, et al.*2024 · 18 citations*](https://md.research.elamur.ai/free/en/papers/narayanan-2024-aviary-training-language-agents/) 11. **[CodePRM: Execution Feedback-enhanced Process Reward Model for Code Generation](https://md.research.elamur.ai/free/en/papers/li-2025-codeprm-execution-feedback-enhanced-process/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Qingyao Li, Xinyi Dai, Xiangyang Li, et al.*2025 · 15 citations* 12. [**Beyond Distillation: Pushing the Limits of Medical LLM Reasoning with Minimalist Rule-Based RL arXiv:2505.17952**Che Liu, Haozhe Wang, Jiazhen Pan, et al.*2025 · 10 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-distillation-pushing-limits-medical-llm/) 13. **[Examining False Positives under Inference Scaling for Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/wang-2025-examining-false-positives-under-inference/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yu Wang, Nan Yang, Liang Wang, et al.*2025 · 9 citations* 14. **[ViLBench: A Suite for Vision-Language Process Reward Modeling](https://md.research.elamur.ai/free/en/papers/tu-2025-vilbench-suite-vision-language-process/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Haoqin Tu, Weitao Feng, Hardy Chen, et al.*2025 · 9 citations* 15. **[MetaScale: Test-Time Scaling with Evolving Meta-Thoughts](https://md.research.elamur.ai/free/en/papers/liu-2026-metascale-test-time-scaling-evolving/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Qin Liu, Wenxuan Zhou, Nan Xu, et al.*2026 · 7 citations* 16. **[Guided by Gut: Efficient Test-Time Scaling with Reinforced Intrinsic Confidence](https://md.research.elamur.ai/free/en/papers/ghasemabadi-2026-guided-gut-efficient-test-time/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Amirhosein Ghasemabadi, Keith G. Mills, Baochun Li, et al.*2026 · 4 citations* 17. **[Process Reward Models Meet Planning: Generating Precise and Scalable Datasets for Step-Level Rewards](https://md.research.elamur.ai/free/en/papers/pisano-2026-process-reward-models-meet-planning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Raffaele Pisano, Roberto Navigli*2026 · 1 citation* 18. **[A Comprehensive Survey of Process Reward Models: Data Generation, Model Construction, and Usage](https://md.research.elamur.ai/free/en/papers/zheng-2026-comprehensive-survey-process-reward-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Congmin Zheng, Jiachen Zhu, Zhuoying Ou, et al.*2026* 19. **[Model-Based Imaginative Planning for Embodied Agents](https://md.research.elamur.ai/free/en/papers/song-2026-model-based-imaginative-planning-embodied/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Junru Song, Hengzhe Jin, Yucong Huang, et al.*2026* 20. **[ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning](https://md.research.elamur.ai/free/en/papers/smirnov-2026-thinkbooster-unified-framework-seamless-test/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Vladislav Smirnov, Quang-Chieu Nguyen, Sergey Senichev, et al.*2026* 28 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/wang-2024-openr-open-source-framework-advanced/) — OpenR: An Open Source Framework for Advanced Reasoning with Large Language Models