--- title: "Who cites Direct Nash Optimization: — 42 citing papers" description: "42 papers of the Elamur Research library cite «Direct Nash Optimization: Teaching Language Models to Self-Improve with General…" canonical: "https://research.elamur.ai/free/en/papers/rosset-2024-direct-nash-optimization-teaching-language/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/rosset-2024-direct-nash-optimization-teaching-language/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Direct Nash Optimization: Teaching Language…](https://md.research.elamur.ai/free/en/papers/rosset-2024-direct-nash-optimization-teaching-language/) / cited by # Who cites «Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences» 42 papers of our library cite it (of the 149,439 whose bibliographies we read) · 17 with a page here, the most cited first · [← back to «Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences»](https://md.research.elamur.ai/free/en/papers/rosset-2024-direct-nash-optimization-teaching-language/) 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. **[Iterative Reasoning Preference Optimization](https://md.research.elamur.ai/free/en/papers/pang-2024-iterative-reasoning-preference-optimization/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Richard Yuanzhe Pang, Weizhe Yuan, Kyunghyun Cho, et al.*2024 · 153 citations* 3. [**Process Reinforcement through Implicit Rewards arXiv:2502.01456**Ganqu Cui, Lifan Yuan, Zefan Wang, et al.*2025 · 151 citations*](https://md.research.elamur.ai/free/en/papers/cui-2025-process-reinforcement-through-implicit-rewards/) 4. [**RLHF Workflow: From Reward Modeling to Online RLHF arXiv:2405.07863**Hanze Dong, Wei Xiong, Bo Pang, et al.*2024 · 111 citations*](https://md.research.elamur.ai/free/en/papers/dong-2024-rlhf-workflow-reward-modeling-online/) 5. [**Nemotron-4 340B Technical Report arXiv:2406.11704**Nvidia: Bo Adler, Niket Agarwal, Ashwath Aithal, et al.*2024 · 107 citations*](https://md.research.elamur.ai/free/en/papers/adler-2024-nemotron-4-340b-technical-report/) 6. **[Disentangling Length from Quality in Direct Preference Optimization](https://md.research.elamur.ai/free/en/papers/park-2024-disentangling-length-quality-direct-preference/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ryan Park, Rafael Rafailov, Stefano Ermon, et al.*2024 · 105 citations* 7. **[Recursive Introspection: Teaching Language Model Agents How to Self-Improve](https://md.research.elamur.ai/free/en/papers/qu-2024-recursive-introspection-teaching-language-model/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yuxiao Qu, Tianjun Zhang, Naman Garg, et al.*2024 · 75 citations* 8. [**Free Process Rewards without Process Labels arXiv:2412.01981**Lifan Yuan, Wendi Li, Huayu Chen, et al.*2024 · 58 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2024-free-process-rewards-process-labels/) 9. [**AgentInstruct: Toward Generative Teaching with Agentic Flows arXiv:2407.03502**Arindam Mitra, Luciano Del Corro, Guoqing Zheng, et al.*2024 · 42 citations*](https://md.research.elamur.ai/free/en/papers/mitra-2024-agentinstruct-toward-generative-teaching-agentic/) 10. **[NaturalReasoning: Reasoning in the Wild with 2.8M Challenging Questions](https://md.research.elamur.ai/free/en/papers/yuan-2025-naturalreasoning-reasoning-wild-2-8m/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Weizhe Yuan, Jane Yu, Song Jiang, et al.*2025 · 26 citations* 11. **[S²R: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/ma-2025-s-2-r-teaching-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ruotian Ma, Peisong Wang, Cheng Liu, et al.*2025 · 26 citations* 12. **[Anchored Preference Optimization and Contrastive Revisions: Addressing Underspecification in Alignment](https://md.research.elamur.ai/free/en/papers/oosterlinck-2025-anchored-preference-optimization-contrastive-revisions/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Karel D'Oosterlinck, Winnie Xu, Chris Develder, et al.*2025 · 20 citations* 13. **[Interactive Evolution: A Neural-Symbolic Self-Training Framework For Large Language Models](https://md.research.elamur.ai/free/en/papers/xu-2025-interactive-evolution-neural-symbolic-self/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Fangzhi Xu, Qiushi Sun, Kanzhi Cheng, et al.*2025 · 15 citations* 14. **[MPO: Multilingual Safety Alignment via Reward Gap Optimization](https://md.research.elamur.ai/free/en/papers/zhao-2025-mpo-multilingual-safety-alignment-reward/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Weixiang Zhao, Yulin Hu, Yang Deng, et al.*2025 · 7 citations* 15. **[CoAct: Co-Active LLM Preference Learning with Human-AI Synergy](https://md.research.elamur.ai/free/en/papers/xu-2026-coact-co-active-llm-preference/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ruiyao Xu, Mihir Parmar, Tiankai Yang, et al.*2026 · 1 citation* 16. **[DynamicFocalPO: Adaptive Focusing Strategy for Preference Optimization](https://md.research.elamur.ai/free/en/papers/zhou-2026-dynamicfocalpo-adaptive-focusing-strategy-preference/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shu Zhou, Junan Chen, Rui Ling, et al.*2026* 17. **[Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment](https://md.research.elamur.ai/free/en/papers/zhang-2026-generalizable-llm-learning-graph-synthetic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yizhuo Zhang, Heng Wang, Shangbin Feng, et al.*2026* 25 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/rosset-2024-direct-nash-optimization-teaching-language/) — Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences