Who cites Andrea Michi
- AlphaEvolve: A coding agent for scientific and algorithmic discoveryarXiv:2506.13131Alexander Novikov, Ngân Vũ, Marvin Eisenberger, et al.2025 · 202 citations
- AI Alignment: A Comprehensive SurveyarXiv:2310.19852Jiaming Ji, Tianyi Qiu, Boyuan Chen, et al.2023 · 173 citations
- 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
- SaulLM-7B: A pioneering Large Language Model for LawarXiv:2403.03883Pierre Colombo, Telmo Pessoa Pires, Malik Boudiaf, et al.2024 · 119 citations
- TextGrad: Automatic "Differentiation" via TextarXiv:2406.07496Mert Yuksekgonul, Federico Bianchi, Joseph Boen, et al.2024 · 96 citations
- A Survey of Reinforcement Learning from Human FeedbackTransactions on Machine Learning ResearchTimo Kaufmann, Paul Weng, Viktor Bengs, et al.2023 · 92 citations
- A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?arXiv:2503.24235Qiyuan Zhang, Fuyuan Lyu, Zexu Sun, et al.2025 · 79 citations
- Scaling Large Language Model-based Multi-Agent CollaborationarXiv:2406.07155Chen Qian, Zihao Xie, YiFei Wang, et al.2024 · 70 citations
- Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective RewardsAnnual Meeting of the Association for Computational Linguistics (ACL)Haoxiang Wang, Yong Lin, Wei Xiong, et al.2024 · 68 citations
- Generative Reward ModelsarXiv:2410.12832Dakota Mahan, Duy Van Phung, Rafael Rafailov, et al.2024 · 58 citations
- RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-FoldConference on Neural Information Processing Systems (NeurIPS)Amrith Setlur, Saurabh Garg, Xinyang Geng, et al.2024 · 56 citations
- Critique-out-Loud Reward ModelsarXiv:2408.11791Zachary Ankner, Mansheej Paul, Brandon Cui, et al.2024 · 52 citations
- Easy-to-Hard Generalization: Scalable Alignment Beyond Human SupervisionConference on Neural Information Processing Systems (NeurIPS)Zhiqing Sun, Longhui Yu, Yikang Shen, et al.2024 · 51 citations
- Fast Best-of-N Decoding via Speculative RejectionConference on Neural Information Processing Systems (NeurIPS)Hanshi Sun, Momin Haider, Ruiqi Zhang, et al.2024 · 50 citations
- Direct Nash Optimization: Teaching Language Models to Self-Improve with General PreferencesarXiv:2404.03715Corby Rosset, Ching-An Cheng, Arindam Mitra, et al.2024 · 44 citations
- A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic SystemsarXiv:2504.09037Zixuan Ke, Fangkai Jiao, Yifei Ming, et al.2025 · 39 citations
- Revisiting the Test-Time Scaling of o1-like Models: Do they Truly Possess Test-Time Scaling Capabilities?Annual Meeting of the Association for Computational Linguistics (ACL)Zhiyuan Zeng, Qinyuan Cheng, Zhangyue Yin, et al.2025 · 38 citations
- Through the Lens of Core Competency: Survey on Evaluation of Large Language ModelsarXiv:2308.07902Ziyu Zhuang, Qiguang Chen, Longxuan Ma, et al.2023 · 35 citations
- Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning PerspectivearXiv:2412.14135Zhiyuan Zeng, Qinyuan Cheng, Zhangyue Yin, et al.2024 · 33 citations
- RMB: Comprehensively Benchmarking Reward Models in LLM AlignmentarXiv:2410.09893Enyu Zhou, Guodong Zheng, Binghai Wang, et al.2024 · 30 citations
- LLM-as-a-Judge & Reward Model: What They Can and Cannot DoarXiv:2409.11239Guijin Son, Hyunwoo Ko, Hoyoung Lee, et al.2024 · 27 citations
- BOND: Aligning LLMs with Best-of-N DistillationarXiv:2407.14622Pier Giuseppe Sessa, Robert Dadashi, Leonard Hussenot, et al.2024 · 23 citations
- Self-Improvement in Language Models: The Sharpening MechanismarXiv:2412.01951Audrey Huang; Adam Block; Dylan J. Foster; Dhruv Rohatgi; Cyril Zhang; Max Simchowitz; Jordan T. Ash;, Akshay Krishnamurthy2024 · 20 citations
- Exploring Data Scaling Trends and Effects in Reinforcement Learning from Human FeedbackarXiv:2503.22230Wei Shen, Guanlin Liu, Zheng Wu, et al.2025 · 12 citations
- AlphaOne: Reasoning Models Thinking Slow and Fast at Test TimeConference on Empirical Methods in Natural Language Processing (EMNLP)Junyu Zhang, Runpei Dong, Han Wang, et al.2025 · 10 citations
- Weight Ensembling Improves Reasoning in Language ModelsarXiv:2504.10478 · COLM 2025Xingyu Dang, Christina Baek, Kaiyue Wen, et al.2025 · 10 citations
- Rethinking Fine-Tuning when Scaling Test-Time Compute: Limiting Confidence Improves Mathematical ReasoningConference on Neural Information Processing Systems (NeurIPS)Feng Chen, Allan Raventos, Nan Cheng, et al.2025 · 9 citations
- Bias Fitting to Mitigate Length Bias of Reward Model in RLHFAnnual Meeting of the Association for Computational Linguistics (ACL)Kangwen Zhao, Jianfeng Cai, Jinhua Zhu, et al.2026 · 2 citations
- Beyond Compromise: Pareto-Lenient Consensus for Efficient Multi-Preference LLM AlignmentFindings of the Association for Computational LinguisticsRenxuan Tan, Rongpeng Li, Zhifeng Zhao, et al.2026 · 1 citation
- Agentic Episodic ControlFindings of the Association for Computational LinguisticsXidong Yang, Wenhao Li, Junjie Sheng, et al.2026
- 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
- Calibrating Inference Time Alignment with Sequence-level Risk AccumulationAnnual Meeting of the Association for Computational Linguistics (ACL)Shanwen Tan, Ziyang Dong, Wei Ju, et al.2026
- Mitigating Cultural Bias in LLMs via Multi-Agent Cultural DebateFindings of the Association for Computational LinguisticsQian Tan, Lei Jiang, Yuting Zeng, et al.2026
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