--- title: "Who cites Nash Learning from Human — 31 citing papers" description: "31 papers of the Elamur Research library cite «Nash Learning from Human Feedback», found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/papers/munos-2023-nash-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/munos-2023-nash-learning-human-feedback/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Nash Learning from Human Feedback](https://md.research.elamur.ai/free/en/papers/munos-2023-nash-learning-human-feedback/) / cited by # Who cites «Nash Learning from Human Feedback» 31 papers of our library cite it (of the 149,439 whose bibliographies we read) · 13 with a page here, the most cited first · [← back to «Nash Learning from Human Feedback»](https://md.research.elamur.ai/free/en/papers/munos-2023-nash-learning-human-feedback/) 1. **[Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts](https://md.research.elamur.ai/free/en/papers/wang-2024-interpretable-preferences-multi-objective-reward/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Haoxiang Wang, Wei Xiong, Tengyang Xie, et al.*2024 · 153 citations* 2. [**SaulLM-7B: A pioneering Large Language Model for Law arXiv:2403.03883**Pierre Colombo, Telmo Pessoa Pires, Malik Boudiaf, et al.*2024 · 119 citations*](https://md.research.elamur.ai/free/en/papers/colombo-2024-saullm-7b-pioneering-large-language/) 3. **[A Survey of Reinforcement Learning from Human Feedback](https://md.research.elamur.ai/free/en/papers/kaufmann-2023-survey-reinforcement-learning-human-feedback/) [Transactions on Machine Learning Research](https://md.research.elamur.ai/free/en/venues/transactions-on-machine-learning-research/)**Timo Kaufmann, Paul Weng, Viktor Bengs, et al.*2023 · 92 citations* 4. **[Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards](https://md.research.elamur.ai/free/en/papers/wang-2024-arithmetic-control-llms-diverse-user/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haoxiang Wang, Yong Lin, Wei Xiong, et al.*2024 · 68 citations* 5. [**Generative Reward Models arXiv:2410.12832**Dakota Mahan, Duy Van Phung, Rafael Rafailov, et al.*2024 · 58 citations*](https://md.research.elamur.ai/free/en/papers/mahan-2024-generative-reward-models/) 6. **[RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold](https://md.research.elamur.ai/free/en/papers/setlur-2024-rl-incorrect-synthetic-data-scales/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Amrith Setlur, Saurabh Garg, Xinyang Geng, et al.*2024 · 56 citations* 7. [**Critique-out-Loud Reward Models arXiv:2408.11791**Zachary Ankner, Mansheej Paul, Brandon Cui, et al.*2024 · 52 citations*](https://md.research.elamur.ai/free/en/papers/ankner-2024-critique-out-loud-reward-models/) 8. **[Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision](https://md.research.elamur.ai/free/en/papers/sun-2024-easy-hard-generalization-scalable-alignment/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhiqing Sun, Longhui Yu, Yikang Shen, et al.*2024 · 51 citations* 9. [**Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences arXiv:2404.03715**Corby Rosset, Ching-An Cheng, Arindam Mitra, et al.*2024 · 44 citations*](https://md.research.elamur.ai/free/en/papers/rosset-2024-direct-nash-optimization-teaching-language/) 10. [**LLM-as-a-Judge & Reward Model: What They Can and Cannot Do arXiv:2409.11239**Guijin Son, Hyunwoo Ko, Hoyoung Lee, et al.*2024 · 27 citations*](https://md.research.elamur.ai/free/en/papers/son-2024-llm-judge-reward-model-they/) 11. [**BOND: Aligning LLMs with Best-of-N Distillation arXiv:2407.14622**Pier Giuseppe Sessa, Robert Dadashi, Leonard Hussenot, et al.*2024 · 23 citations*](https://md.research.elamur.ai/free/en/papers/sessa-2024-bond-aligning-llms-best-n/) 12. **[Beyond Compromise: Pareto-Lenient Consensus for Efficient Multi-Preference LLM Alignment](https://md.research.elamur.ai/free/en/papers/tan-2026-beyond-compromise-pareto-lenient-consensus/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Renxuan Tan, Rongpeng Li, Zhifeng Zhao, et al.*2026 · 1 citation* 13. **[Mitigating Cultural Bias in LLMs via Multi-Agent Cultural Debate](https://md.research.elamur.ai/free/en/papers/tan-2026-mitigating-cultural-bias-llms-multi/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Qian Tan, Lei Jiang, Yuting Zeng, et al.*2026* 18 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/munos-2023-nash-learning-human-feedback/) — Nash Learning from Human Feedback