🔗 Who cites «Scaling Laws for Reward Model Overoptimization»

268 papers of our library cite it (of the 141,840 whose bibliographies we read) · 33 with a page here, the most cited first · ← back to «Scaling Laws for Reward Model Overoptimization»

1. DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement LearningNatureDeepSeek-AI, Daya Guo, Dejian Yang, et al.2025 · 4,593 citations
2. Let's Verify Step by SteparXiv:2305.20050Hunter Lightman, Vineet Kosaraju, Yura Burda, et al.2023 · 1,361 citations
3. Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language ModelsComputational LinguisticsYue Zhang, Yafu Li, Leyang Cui, et al.2023 · 966 citations
4. Constitutional AI: Harmlessness from AI FeedbackarXiv:2212.08073Yuntao Bai, Saurav Kadavath, Sandipan Kundu, et al.2022 · 962 citations
5. DAPO: An Open-Source LLM Reinforcement Learning System at ScalearXiv:2503.14476Qiying Yu, Zheng Zhang, Ruofei Zhu, et al.2025 · 634 citations
6. AlpacaFarm: A Simulation Framework for Methods that Learn from Human FeedbackConference on Neural Information Processing Systems (NeurIPS)Yann Dubois, Xuechen Li, Rohan Taori, et al.2023 · 424 citations
7. Towards Understanding Sycophancy in Language ModelsarXiv:2310.13548Mrinank Sharma, Meg Tong, Tomasz Korbak, et al.2023 · 355 citations
8. Discovering Language Model Behaviors with Model-Written EvaluationsarXiv:2212.09251Ethan Perez, Sam Ringer, Kamilė Lukošiūtė, et al.2022 · 307 citations
9. InternLM2 Technical ReportarXiv:2403.17297Zheng Cai, Maosong Cao, Haojiong Chen, et al.2024 · 299 citations
10. Scaling Relationship on Learning Mathematical Reasoning with Large Language ModelsarXiv:2308.01825Zheng Yuan, Hongyi Yuan, Chengpeng Li, et al.2023 · 252 citations
11. RAFT: Reward rAnked FineTuning for Generative Foundation Model AlignmentarXiv:2304.06767Hanze Dong, Wei Xiong, Deepanshu Goyal, et al.2023 · 239 citations
12. AlpaGasus: Training A Better Alpaca with Fewer DataarXiv:2307.08701Lichang Chen, Shiyang Li, Jun Yan, et al.2023 · 215 citations
13. Reinforced Self-Training (ReST) for Language ModelingarXiv:2308.08998Caglar Gulcehre, Tom Le Paine, Srivatsan Srinivasan, et al.2023 · 209 citations
14. RRHF: Rank Responses to Align Language Models with Human Feedback without tearsarXiv:2304.05302Zheng Yuan, Hongyi Yuan, Chuanqi Tan, et al.2023 · 194 citations
15. Preference Ranking Optimization for Human AlignmentProceedings of the AAAI Conference on Artificial IntelligenceFeifan Song, Bowen Yu, Minghao Li, et al.2023 · 183 citations
16. Process Reinforcement through Implicit RewardsarXiv:2502.01456Ganqu Cui, Lifan Yuan, Zefan Wang, et al.2025 · 147 citations
17. How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data CompositionAnnual Meeting of the Association for Computational Linguistics (ACL)Guanting Dong, Hongyi Yuan, Keming Lu, et al.2024 · 145 citations
18. 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 · 142 citations
19. Understanding the Effects of RLHF on LLM Generalisation and DiversityarXiv:2310.06452Robert Kirk, Ishita Mediratta, Christoforos Nalmpantis, et al.2023 · 131 citations
20. Large Language Model Alignment: A SurveyarXiv:2309.15025Tianhao Shen, Renren Jin, Yufei Huang, et al.2023 · 123 citations
21. Generative Judge for Evaluating AlignmentarXiv:2310.05470Junlong Li, Shichao Sun, Weizhe Yuan, et al.2023 · 121 citations
22. Fine-tuning Language Models for FactualityarXiv:2311.08401Katherine Tian, Eric Mitchell, Huaxiu Yao, et al.2023 · 120 citations
23. Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language ModelsarXiv:2408.00724Yangzhen Wu, Zhiqing Sun, Shanda Li, et al.2024 · 114 citations
24. Skywork-Reward: Bag of Tricks for Reward Modeling in LLMsarXiv:2410.18451Chris Yuhao Liu, Liang Zeng, Jiacai Liu, et al.2024 · 104 citations
25. Statistical Rejection Sampling Improves Preference OptimizationarXiv:2309.06657Tianqi Liu, Yao Zhao, Rishabh Joshi, et al.2023 · 101 citations
26. Disentangling Length from Quality in Direct Preference OptimizationFindings of the Association for Computational LinguisticsRyan Park, Rafael Rafailov, Stefano Ermon, et al.2024 · 99 citations
27. Inverse Scaling: When Bigger Isn't BetterarXiv:2306.09479 · Transactions on Machine Learning Research (TMLR), 10/2023, https://openreview.net/forum?id=DwgRm72GQFIan R. McKenzie, Alexander Lyzhov, Michael Pieler, et al.2023 · 99 citations
28. A Survey on Data Selection for Language ModelsarXiv:2402.16827Alon Albalak, Yanai Elazar, Sang Michael Xie, et al.2024 · 85 citations
29. Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language GenerationTransactions of the Association for Computational Linguistics (TACL)Patrick Fernandes, Aman Madaan, Emmy Liu, et al.2023 · 84 citations
30. Mitigating the Alignment Tax of RLHFConference on Empirical Methods in Natural Language Processing (EMNLP)Yong Lin, Hangyu Lin, Wei Xiong, et al.2024 · 82 citations
31. Inference-Time Scaling for Generalist Reward ModelingarXiv:2504.02495Zijun Liu, Peiyi Wang, Runxin Xu, et al.2025 · 64 citations
32. Constituency Parsing using LLMsarXiv:2310.19462Xuefeng Bai, Jialong Wu, Yulong Chen, et al.2023 · 14 citations
33. Auditing Discovery Claims: A Two-Sided Criterion for Agentic Science, with the Negative Side DecidablearXiv:2608.00981Wenhui Chen, Jianlin Chen, Ziyao Lin, et al.2026

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