🔗 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»

1. Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-ExpertsFindings of the Association for Computational LinguisticsHaoxiang Wang, Wei Xiong, Tengyang Xie, et al.2024 · 153 citations
2. Iterative Reasoning Preference OptimizationConference on Neural Information Processing Systems (NeurIPS)Richard Yuanzhe Pang, Weizhe Yuan, Kyunghyun Cho, et al.2024 · 153 citations
3. Process Reinforcement through Implicit RewardsarXiv:2502.01456Ganqu Cui, Lifan Yuan, Zefan Wang, et al.2025 · 151 citations
4. RLHF Workflow: From Reward Modeling to Online RLHFarXiv:2405.07863Hanze Dong, Wei Xiong, Bo Pang, et al.2024 · 111 citations
5. Nemotron-4 340B Technical ReportarXiv:2406.11704Nvidia: Bo Adler, Niket Agarwal, Ashwath Aithal, et al.2024 · 107 citations
6. Disentangling Length from Quality in Direct Preference OptimizationFindings of the Association for Computational LinguisticsRyan Park, Rafael Rafailov, Stefano Ermon, et al.2024 · 105 citations
7. Recursive Introspection: Teaching Language Model Agents How to Self-ImproveConference on Neural Information Processing Systems (NeurIPS)Yuxiao Qu, Tianjun Zhang, Naman Garg, et al.2024 · 75 citations
8. Free Process Rewards without Process LabelsarXiv:2412.01981Lifan Yuan, Wendi Li, Huayu Chen, et al.2024 · 58 citations
9. AgentInstruct: Toward Generative Teaching with Agentic FlowsarXiv:2407.03502Arindam Mitra, Luciano Del Corro, Guoqing Zheng, et al.2024 · 42 citations
10. NaturalReasoning: Reasoning in the Wild with 2.8M Challenging QuestionsConference on Neural Information Processing Systems (NeurIPS)Weizhe Yuan, Jane Yu, Song Jiang, et al.2025 · 26 citations
11. S²R: Teaching LLMs to Self-verify and Self-correct via Reinforcement LearningAnnual Meeting of the Association for Computational Linguistics (ACL)Ruotian Ma, Peisong Wang, Cheng Liu, et al.2025 · 26 citations
12. Anchored Preference Optimization and Contrastive Revisions: Addressing Underspecification in AlignmentTransactions of the Association for Computational Linguistics (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 ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Fangzhi Xu, Qiushi Sun, Kanzhi Cheng, et al.2025 · 15 citations
14. MPO: Multilingual Safety Alignment via Reward Gap OptimizationAnnual Meeting of the Association for Computational Linguistics (ACL)Weixiang Zhao, Yulin Hu, Yang Deng, et al.2025 · 7 citations
15. CoAct: Co-Active LLM Preference Learning with Human-AI SynergyAnnual Meeting of the Association for Computational Linguistics (ACL)Ruiyao Xu, Mihir Parmar, Tiankai Yang, et al.2026 · 1 citation
16. DynamicFocalPO: Adaptive Focusing Strategy for Preference OptimizationFindings of the Association for Computational LinguisticsShu Zhou, Junan Chen, Rui Ling, et al.2026
17. Generalizable LLM Learning of Graph Synthetic Data with Post-training AlignmentFindings of the Association for Computational LinguisticsYizhuo Zhang, Heng Wang, Shangbin Feng, et al.2026

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