---
title: "Who cites Andrea Michi — 77 papers of our library"
description: "77 papers of the Elamur Research library cite the works of Andrea Michi, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/andrea-michi/cited-by/"
---
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# Who cites Andrea Michi

77 citations of their works from 77 papers of our library (of the 149,439 whose bibliographies we read) · 33 with a page here, the most cited first · [← Andrea Michi](https://md.research.elamur.ai/free/en/authors/andrea-michi/)

1. [**AlphaEvolve: A coding agent for scientific and algorithmic discovery arXiv:2506.13131**Alexander Novikov, Ngân Vũ, Marvin Eisenberger, et al.*2025 · 202 citations*](https://md.research.elamur.ai/free/en/papers/novikov-2025-alphaevolve-coding-agent-scientific-algorithmic/)
2. [**AI Alignment: A Comprehensive Survey arXiv:2310.19852**Jiaming Ji, Tianyi Qiu, Boyuan Chen, et al.*2023 · 173 citations*](https://md.research.elamur.ai/free/en/papers/ji-2023-ai-alignment-comprehensive-survey/)
3. **[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*
4. [**SaulLM-7B: A pioneering Large Language Model for Law arXiv:2403.03883**Pierre Colombo, Telmo Pessoa Pires, Malik Boudiaf, et al.*2024 · 119 citations*](https://md.research.elamur.ai/free/en/papers/colombo-2024-saullm-7b-pioneering-large-language/)
5. [**TextGrad: Automatic "Differentiation" via Text arXiv:2406.07496**Mert Yuksekgonul, Federico Bianchi, Joseph Boen, et al.*2024 · 96 citations*](https://md.research.elamur.ai/free/en/papers/yuksekgonul-2024-textgrad-automatic-differentiation-text/)
6. **[A Survey of Reinforcement Learning from Human Feedback](https://md.research.elamur.ai/free/en/papers/kaufmann-2023-survey-reinforcement-learning-human-feedback/) [Transactions on Machine Learning Research](https://md.research.elamur.ai/free/en/venues/transactions-on-machine-learning-research/)**Timo Kaufmann, Paul Weng, Viktor Bengs, et al.*2023 · 92 citations*
7. [**A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well? arXiv:2503.24235**Qiyuan Zhang, Fuyuan Lyu, Zexu Sun, et al.*2025 · 79 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2025-survey-test-time-scaling-large/)
8. [**Scaling Large Language Model-based Multi-Agent Collaboration arXiv:2406.07155**Chen Qian, Zihao Xie, YiFei Wang, et al.*2024 · 70 citations*](https://md.research.elamur.ai/free/en/papers/qian-2024-scaling-large-language-model/)
9. **[Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards](https://md.research.elamur.ai/free/en/papers/wang-2024-arithmetic-control-llms-diverse-user/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haoxiang Wang, Yong Lin, Wei Xiong, et al.*2024 · 68 citations*
10. [**Generative Reward Models arXiv:2410.12832**Dakota Mahan, Duy Van Phung, Rafael Rafailov, et al.*2024 · 58 citations*](https://md.research.elamur.ai/free/en/papers/mahan-2024-generative-reward-models/)
11. **[RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold](https://md.research.elamur.ai/free/en/papers/setlur-2024-rl-incorrect-synthetic-data-scales/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Amrith Setlur, Saurabh Garg, Xinyang Geng, et al.*2024 · 56 citations*
12. [**Critique-out-Loud Reward Models arXiv:2408.11791**Zachary Ankner, Mansheej Paul, Brandon Cui, et al.*2024 · 52 citations*](https://md.research.elamur.ai/free/en/papers/ankner-2024-critique-out-loud-reward-models/)
13. **[Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision](https://md.research.elamur.ai/free/en/papers/sun-2024-easy-hard-generalization-scalable-alignment/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhiqing Sun, Longhui Yu, Yikang Shen, et al.*2024 · 51 citations*
14. **[Fast Best-of-N Decoding via Speculative Rejection](https://md.research.elamur.ai/free/en/papers/sun-2024-fast-best-n-decoding-speculative/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Hanshi Sun, Momin Haider, Ruiqi Zhang, et al.*2024 · 50 citations*
15. [**Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences arXiv:2404.03715**Corby Rosset, Ching-An Cheng, Arindam Mitra, et al.*2024 · 44 citations*](https://md.research.elamur.ai/free/en/papers/rosset-2024-direct-nash-optimization-teaching-language/)
16. [**A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems arXiv:2504.09037**Zixuan Ke, Fangkai Jiao, Yifei Ming, et al.*2025 · 39 citations*](https://md.research.elamur.ai/free/en/papers/ke-2025-survey-frontiers-llm-reasoning-inference/)
17. **[Revisiting the Test-Time Scaling of o1-like Models: Do they Truly Possess Test-Time Scaling Capabilities?](https://md.research.elamur.ai/free/en/papers/zeng-2025-revisiting-test-time-scaling-o1/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhiyuan Zeng, Qinyuan Cheng, Zhangyue Yin, et al.*2025 · 38 citations*
18. [**Through the Lens of Core Competency: Survey on Evaluation of Large Language Models arXiv:2308.07902**Ziyu Zhuang, Qiguang Chen, Longxuan Ma, et al.*2023 · 35 citations*](https://md.research.elamur.ai/free/en/papers/zhuang-2023-through-lens-core-competency-survey/)
19. [**Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective arXiv:2412.14135**Zhiyuan Zeng, Qinyuan Cheng, Zhangyue Yin, et al.*2024 · 33 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2024-scaling-search-learning-roadmap-reproduce/)
20. [**RMB: Comprehensively Benchmarking Reward Models in LLM Alignment arXiv:2410.09893**Enyu Zhou, Guodong Zheng, Binghai Wang, et al.*2024 · 30 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2024-rmb-comprehensively-benchmarking-reward-models/)
21. [**LLM-as-a-Judge & Reward Model: What They Can and Cannot Do arXiv:2409.11239**Guijin Son, Hyunwoo Ko, Hoyoung Lee, et al.*2024 · 27 citations*](https://md.research.elamur.ai/free/en/papers/son-2024-llm-judge-reward-model-they/)
22. [**BOND: Aligning LLMs with Best-of-N Distillation arXiv:2407.14622**Pier Giuseppe Sessa, Robert Dadashi, Leonard Hussenot, et al.*2024 · 23 citations*](https://md.research.elamur.ai/free/en/papers/sessa-2024-bond-aligning-llms-best-n/)
23. [**Self-Improvement in Language Models: The Sharpening Mechanism arXiv:2412.01951**Audrey Huang; Adam Block; Dylan J. Foster; Dhruv Rohatgi; Cyril Zhang; Max Simchowitz; Jordan T. Ash;, Akshay Krishnamurthy*2024 · 20 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-self-improvement-language-models-sharpening/)
24. [**Exploring Data Scaling Trends and Effects in Reinforcement Learning from Human Feedback arXiv:2503.22230**Wei Shen, Guanlin Liu, Zheng Wu, et al.*2025 · 12 citations*](https://md.research.elamur.ai/free/en/papers/shen-2025-exploring-data-scaling-trends-effects/)
25. **[AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time](https://md.research.elamur.ai/free/en/papers/zhang-2025-alphaone-reasoning-models-thinking-slow/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Junyu Zhang, Runpei Dong, Han Wang, et al.*2025 · 10 citations*
26. [**Weight Ensembling Improves Reasoning in Language Models arXiv:2504.10478 · COLM 2025**Xingyu Dang, Christina Baek, Kaiyue Wen, et al.*2025 · 10 citations*](https://md.research.elamur.ai/free/en/papers/dang-2025-weight-ensembling-improves-reasoning-language/)
27. **[Rethinking Fine-Tuning when Scaling Test-Time Compute: Limiting Confidence Improves Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/chen-2025-rethinking-fine-tuning-scaling-test/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Feng Chen, Allan Raventos, Nan Cheng, et al.*2025 · 9 citations*
28. **[Bias Fitting to Mitigate Length Bias of Reward Model in RLHF](https://md.research.elamur.ai/free/en/papers/zhao-2026-bias-fitting-mitigate-length-bias/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kangwen Zhao, Jianfeng Cai, Jinhua Zhu, et al.*2026 · 2 citations*
29. **[Beyond Compromise: Pareto-Lenient Consensus for Efficient Multi-Preference LLM Alignment](https://md.research.elamur.ai/free/en/papers/tan-2026-beyond-compromise-pareto-lenient-consensus/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Renxuan Tan, Rongpeng Li, Zhifeng Zhao, et al.*2026 · 1 citation*
30. **[Agentic Episodic Control](https://md.research.elamur.ai/free/en/papers/yang-2026-agentic-episodic-control/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xidong Yang, Wenhao Li, Junjie Sheng, et al.*2026*
31. [**Auditing Discovery Claims: A Two-Sided Criterion for Agentic Science, with the Negative Side Decidable arXiv:2608.00981**Wenhui Chen, Jianlin Chen, Ziyao Lin, et al.*2026*](https://md.research.elamur.ai/free/en/papers/chen-2026-auditing-discovery-claims-two/)
32. **[Calibrating Inference Time Alignment with Sequence-level Risk Accumulation](https://md.research.elamur.ai/free/en/papers/tan-2026-calibrating-inference-time-alignment-sequence/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shanwen Tan, Ziyang Dong, Wei Ju, et al.*2026*
33. **[Mitigating Cultural Bias in LLMs via Multi-Agent Cultural Debate](https://md.research.elamur.ai/free/en/papers/tan-2026-mitigating-cultural-bias-llms-multi/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Qian Tan, Lei Jiang, Yuting Zeng, et al.*2026*

44 more citing papers of our library have no page here yet.

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