🧑‍🔬 Who cites Sarah Perrin

34 citations of their works from 34 papers of our library (of the 149,439 whose bibliographies we read) · 19 with a page here, the most cited first · ← Sarah Perrin

1. Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRsarXiv:2503.01307Kanishk Gandhi, Ayush Chakravarthy, Anikait Singh, et al.2025 · 123 citations
2. Multi-Step Reasoning with Large Language Models, a SurveyACM Computing SurveysAske Plaat, Annie Wong, Suzan Verberne, et al.2024 · 106 citations
3. 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
4. Scaling Large Language Model-based Multi-Agent CollaborationarXiv:2406.07155Chen Qian, Zihao Xie, YiFei Wang, et al.2024 · 70 citations
5. 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
6. Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-ThoughtarXiv:2501.04682Violet Xiang, Charlie Snell, Kanishk Gandhi, et al.2025 · 47 citations
7. 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
8. 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
9. 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
10. RMB: Comprehensively Benchmarking Reward Models in LLM AlignmentarXiv:2410.09893Enyu Zhou, Guodong Zheng, Binghai Wang, et al.2024 · 30 citations
11. RWKV-7 "Goose" with Expressive Dynamic State EvolutionarXiv:2503.14456Bo Peng, Ruichong Zhang, Daniel Goldstein, et al.2025 · 23 citations
12. 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
13. Exploring Data Scaling Trends and Effects in Reinforcement Learning from Human FeedbackarXiv:2503.22230Wei Shen, Guanlin Liu, Zheng Wu, et al.2025 · 12 citations
14. 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
15. Weight Ensembling Improves Reasoning in Language ModelsarXiv:2504.10478 · COLM 2025Xingyu Dang, Christina Baek, Kaiyue Wen, et al.2025 · 10 citations
16. 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
17. BoxingGym: Benchmarking Progress in Automated Experimental Design and Model DiscoveryarXiv:2501.01540Kanishk Gandhi, Michael Y. Li, Lyle Goodyear, et al.2025 · 4 citations
18. 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
19. 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

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