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Large Language Models (LLMs) have revolutionized various domains, including natural language processing, data analysis, and software development, by enabling automation.
Bandit based monte-carlo planning
Levente Kocsis and Csaba Szepesvári. 2006 · 2006
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Efficient selectivity and backup operators in monte-carlo tree search
Rémi Coulom. 2007 · 2007
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al. 2016 · 2016
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Language modeling with gated convolutional networks
Yann N Dauphin, Angela Fan, Michael Auli, and David Grangier. 2017 · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017 · 2017
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Mastering chess and shogi by self-play with a general reinforcement learning algorithm
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al. 2017 · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2019 · 2019
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Lora: Low-rank adaptation of large language models
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021 · 2021
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Show your work: Scratchpads for intermediate computation with language models
Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, Charles Sutton, and Augustus Odena. 2021 · 2021
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Deepspeed inference: Enabling efficient inference of transformer models at unprecedented scale
Reza Yazdani Aminabadi, Samyam Rajbhandari, Minjia Zhang, Ammar Ahmad Awan, Cheng Li, Du Li, Elton Zheng, Jeff Rasley, Shaden Smith, Olatunji Ruwase, and Yuxiong He. 2022 · 2022
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
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React: Synergizing reasoning and acting in language models
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. 2022 · 2022
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Flashattention-2: Faster attention with better parallelism and work partitioning
Tri Dao. 2023 · 2023
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Swe-bench: Can language models resolve real-world github issues?
Carlos E Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan. 2023 · 2023
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Efficient memory management for large language model serving with pagedattention
Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph Gonzalez, Hao Zhang, and Ion Stoica. 2023 · 2023
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Rope + bias = better length extrapolation
Jianlin Su. 2023 · 2023
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Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, et al. 2023 · 2023
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou. 2023 · 2023
Cited alongside, same era.
Swe-bench lite
Aider. 2024 · 2024
Cited alongside, same era.
Critique-out-loud reward models
Zachary Ankner, Mansheej Paul, Brandon Cui, Jonathan D Chang, and Prithviraj Ammanabrolu. 2024 · 2024
Cited alongside, same era.
Swe-search: Enhancing software agents with monte carlo tree search and iterative refinement
Agentinstruct: Toward generative teaching with agentic flows
Arindam Mitra, Luciano Del Corro, Guoqing Zheng, Shweti Mahajan, Dany Rouhana, Andres Codas, Yadong Lu, Wei-ge Chen, Olga Vrousgos, Corby Rosset, et al. 2024 · 2024
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moatless-tools
Adam Orwall. 2024 · 2024
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Repograph: Enhancing ai software engineering with repository-level code graph
Siru Ouyang, Wenhao Yu, Kaixin Ma, Zilin Xiao, Zhihan Zhang, Mengzhao Jia, Jiawei Han, Hongming Zhang, and Dong Yu. 2024 · 2024
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Mutual reasoning makes smaller llms stronger problem-solvers
Zhenting Qi, Mingyuan Ma, Jiahang Xu, Li Lyna Zhang, Fan Yang, and Mao Yang. 2024 · 2024
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. 2024 · 2024
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Antonis Antoniades, Albert Örwall, Kexun Zhang, Yuxi Xie, Anirudh Goyal, and William Wang. 2024 · 2024
Cited alongside, same era.
Masai: Modular architecture for software-engineering ai agents
Daman Arora, Atharv Sonwane, Nalin Wadhwa, Abhav Mehrotra, Saiteja Utpala, Ramakrishna Bairi, Aditya Kanade, and Nagarajan Natarajan. 2024 · 2024
Cited alongside, same era.
Scaling data collection for training software engineering agents
Ibragim Badertdinov, Maria Trofimova, Yuri Anapolskiy, Sergey Abramov, Karina Zainullina, Alexander Golubev, Sergey Polezhaev, Daria Litvintseva, Simon Karasik, Filipp Fisin, Sergey Skvortsov, Maxim Nekrashevich, Anton Shevtsov, and Boris Yangel. 2024 · 2024
Cited alongside, same era.
Graph of thoughts: Solving elaborate problems with large language models
Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Michal Podstawski, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Hubert Niewiadomski, Piotr Nyczyk, et al. 2024 · 2024
Cited alongside, same era.
Large language monkeys: Scaling inference compute with repeated sampling
Bradley Brown, Jordan Juravsky, Ryan Ehrlich, Ronald Clark, Quoc V Le, Christopher Ré, and Azalia Mirhoseini. 2024 · 2024
Cited alongside, same era.
Rlef: Grounding code llms in execution feedback with reinforcement learning
Jonas Gehring, Kunhao Zheng, Jade Copet, Vegard Mella, Taco Cohen, and Gabriel Synnaeve. 2024 · 2024
Cited alongside, same era.
Grag: Graph retrieval-augmented generation
Yuntong Hu, Zhihan Lei, Zheng Zhang, Bo Pan, Chen Ling, and Liang Zhao. 2024 · 2024
Cited alongside, same era.
Pre-rmsnorm and pre-crmsnorm transformers: equivalent and efficient pre-ln transformers
Zixuan Jiang, Jiaqi Gu, Hanqing Zhu, and David Pan. 2024 · 2024
Cited alongside, same era.
Later among the works it cites.
Llm-as-a-judge & reward model: What they can and cannot do
Guijin Son, Hyunwoo Ko, Hoyoung Lee, Yewon Kim, and Seunghyeok Hong. 2024 · 2024
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Roformer: Enhanced transformer with rotary position embedding
Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu. 2024 · 2024
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Toward self-improvement of llms via imagination, searching, and criticizing
Ye Tian, Baolin Peng, Linfeng Song, Lifeng Jin, Dian Yu, Haitao Mi, and Dong Yu. 2024 · 2024
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Towards neuro-symbolic models of language cognition: Llms as proposers and evaluators
Polina Tsvilodub, Fausto Carcassi, and Michael Franke. 2024 · 2024
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Thinking llms: General instruction following with thought generation
Tianhao Wu, Janice Lan, Weizhe Yuan, Jiantao Jiao, Jason Weston, and Sainbayar Sukhbaatar. 2024 · 2024
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Agentless: Demystifying llm-based software engineering agents
Chunqiu Steven Xia, Yinlin Deng, Soren Dunn, and Lingming Zhang. 2024 · 2024
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Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. 2024 · 2024
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Announcing claude 3.5 sonnet
Anthropic. 2024a · 2025
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Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
DeepSeek-AI et al. 2025 · 2025
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rstar-math: Small llms can master math reasoning with self-evolved deep thinking
Xinyu Guan, Li Lyna Zhang, Yifei Liu, Ning Shang, Youran Sun, Yi Zhu, Fan Yang, and Mao Yang. 2025 · 2025
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Hello gpt-4o
OpenAI. 2025 · 2025
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