--- title: "Who cites A Survey of Reinforcement — 57 citing papers" description: "57 papers of the Elamur Research library cite «A Survey of Reinforcement Learning from Human Feedback», found in their bibliographies…" canonical: "https://research.elamur.ai/free/en/papers/kaufmann-2023-survey-reinforcement-learning-human-feedback/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/kaufmann-2023-survey-reinforcement-learning-human-feedback/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [A Survey of Reinforcement Learning from Human…](https://md.research.elamur.ai/free/en/papers/kaufmann-2023-survey-reinforcement-learning-human-feedback/) / cited by # Who cites «A Survey of Reinforcement Learning from Human Feedback» 57 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 «A Survey of Reinforcement Learning from Human Feedback»](https://md.research.elamur.ai/free/en/papers/kaufmann-2023-survey-reinforcement-learning-human-feedback/) 1. [**The Llama 3 Herd of Models arXiv:2407.21783**Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, et al.*2024 · 7,682 citations*](https://md.research.elamur.ai/free/en/papers/grattafiori-2024-llama-3-herd-models/) 2. [**HybridFlow: A Flexible and Efficient RLHF Framework arXiv:2409.19256**Guangming Sheng, Chi Zhang, Zilingfeng Ye, et al.*2024 · 624 citations*](https://md.research.elamur.ai/free/en/papers/sheng-2024-hybridflow-flexible-efficient-rlhf-framework/) 3. [**Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning arXiv:2503.09516**Bowen Jin, Hansi Zeng, Zhenrui Yue, et al.*2025 · 322 citations*](https://md.research.elamur.ai/free/en/papers/jin-2025-search-r1-training-llms-reason/) 4. [**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/) 5. [**Secrets of RLHF in Large Language Models Part II: Reward Modeling arXiv:2401.06080**Binghai Wang, Rui Zheng, Lu Chen, et al.*2024 · 72 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-secrets-rlhf-large-language-models/) 6. **[Safer or Luckier? LLMs as Safety Evaluators Are Not Robust to Artifacts](https://md.research.elamur.ai/free/en/papers/chen-2025-safer-or-luckier-llms-as/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hongyu Chen, Seraphina Goldfarb-Tarrant*2025 · 13 citations* 7. [**EventRL: Enhancing Event Extraction with Outcome Supervision for Large Language Models arXiv:2402.11430**Jun Gao, Huan Zhao, Wei Wang, et al.*2024 · 11 citations*](https://md.research.elamur.ai/free/en/papers/gao-2024-eventrl-enhancing-event-extraction-outcome/) 8. **[A Survey of Inductive Reasoning for Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2026-survey-inductive-reasoning-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kedi Chen, Dezhao Ruan, Yuhao Dan, et al.*2026 · 10 citations* 9. **[AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction](https://md.research.elamur.ai/free/en/papers/tsang-2026-autograph-r1-end-end-reinforcement/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hong Ting Tsang, Jiaxin Bai, Haoyu Huang, et al.*2026 · 4 citations* 10. **[ProMed: Shapley Information Gain Guided Reinforcement Learning for Proactive Medical LLMs](https://md.research.elamur.ai/free/en/papers/ding-2026-promed-shapley-information-gain-guided/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hongxin Ding, Baixiang Huang, Yue Fang, et al.*2026 · 4 citations* 11. **[OpenGenAlign: A Preference Dataset and Benchmark for Trustworthy Reward Modeling in Open-Ended, Long-Context Generation](https://md.research.elamur.ai/free/en/papers/zhang-2026-opengenalign-preference-dataset-benchmark-trustworthy/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hanning Zhang, Juntong Song, Juno Zhu, et al.*2026 · 2 citations* 12. **[Position: From Noise to Signal to Selbstzweck - Reframing Human Label Variation in the Era of Post-training in NLP](https://md.research.elamur.ai/free/en/papers/xu-2026-position-noise-signal-selbstzweck-reframing/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shanshan Xu, Santosh T.Y.S.S, Barbara Plank*2026 · 1 citation* 13. **[CARL: Constraint-Aware Reinforcement Learning for Planning with LLMs](https://md.research.elamur.ai/free/en/papers/qi-2026-carl-constraint-aware-reinforcement-learning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Qiuyi Qi, Jinjian Zhang, Mutian Bao, et al.*2026* 14. **[From Answers to Arguments: Toward Trustworthy Clinical Diagnostic Reasoning with Toulmin-Guided Curriculum Goal-Conditioned Learning](https://md.research.elamur.ai/free/en/papers/zhan-2026-answers-arguments-trustworthy-clinical-diagnostic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chen Zhan, Xiaoyu Tan, Gengchen Ma, et al.*2026* 15. **[Improving LLM Domain Certification with Pretrained Guide Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-improving-llm-domain-certification-pretrained/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Jiaqian Zhang, Zhaozhi Qian, Faroq AL-Tam, et al.*2026* 16. **[Joint Optimization of Training Data and Policy in RLHF](https://md.research.elamur.ai/free/en/papers/yu-2026-joint-optimization-training-data-policy/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhuohao Yu, Jiali Zeng, Weizheng Gu, et al.*2026* 17. **[PARL: Prompt-based Agents for Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/resendiz-2026-parl-prompt-based-agents-reinforcement/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Yarik Menchaca Resendiz, Roman Klinger*2026* 18. **[See2Refine: Vision-Language Feedback Improves LLM-Based eHMI Action Designers](https://md.research.elamur.ai/free/en/papers/xia-2026-see2refine-vision-language-feedback-improves/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ding Xia, Xinyue Gui, Mark Colley, et al.*2026* 19. **[Too Open for Opinion? Embracing Open-Endedness in Large Language Models for Social Simulation](https://md.research.elamur.ai/free/en/papers/ma-2026-too-open-opinion-embracing-open/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Bolei Ma, Yong Cao, Indira Sen, et al.*2026* 38 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/kaufmann-2023-survey-reinforcement-learning-human-feedback/) — A Survey of Reinforcement Learning from Human Feedback