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Large-scale generative models like DeepSeek-R1 and OpenAI-O1 benefit substantially from chain-of-thought (CoT) reasoning, yet pushing their performance typically requires vast data, large model sizes, and full-parameter fine-tuning.
Thinking, fast and slow
Daniel Kahneman. 2011 · 2011
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Grady Booch, F. Fabiano, L. Horesh, Kiran Kate, Jonathan Lenchner, Nick Linck, Andrea Loreggia, Keerthiram Murugesan, Nicholas Mattei, Francesca Rossi, and Biplav Srivastava. 2020 · 2020
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, and 39 others. 2021 · 2021
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Training verifiers to solve math word problems
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Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2021 · 2021
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Lora: Low-rank adaptation of large language models
J. Edward Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, and Weizhu Chen. 2021 · 2021
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Human-like intuitive behavior and reasoning biases emerged in large language models but disappeared in chatgpt
Thilo Hagendorff, Sarah Fabi, and Michal Kosinski. 2022 · 2022
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Code alpaca: An instruction-following llama model for code generation
Sahil Chaudhary. 2023 · 2023
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Free dolly: Introducing the world’s first truly open instruction-tuned llm
Mike Conover, Matt Hayes, Ankit Mathur, Jianwei Xie, Jun Wan, Sam Shah, Ali Ghodsi, Patrick Wendell, Matei Zaharia, and Reynold Xin. 2023 · 2023
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Qlora: Efficient finetuning of quantized llms
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. 2023 · 2023
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Sparsegpt: Massive language models can be accurately pruned in one-shot
Elias Frantar and Dan Alistarh. 2023 · 2023
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Platypus: Quick, cheap, and powerful refinement of llms
Ariel N. Lee, Cole J. Hunter, and Nataniel Ruiz. 2023 · 2023
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Llm-pruner: On the structural pruning of large language models
Xinyin Ma, Gongfan Fang, and Xinchao Wang. 2023 · 2023
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A simple and effective pruning approach for large language models
Mingjie Sun, Zhuang Liu, Anna Bair, and J. Zico Kolter. 2023 · 2023
Lora+: Efficient low rank adaptation of large models
Soufiane Hayou, Nikhil Ghosh, and Bin Yu. 2024 · 2024
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Safety layers in aligned large language models: The key to llm security
Shen Li, Liuyi Yao, Lan Zhang, and Yaliang Li. 2024 · 2024
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Pissa: Principal singular values and singular vectors adaptation of large language models
Fanxu Meng, Zhaohui Wang, and Muhan Zhang. 2024 · 2024
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Dynathink: Fast or slow? a dynamic decision-making framework for large language models
Jiabao Pan, Yan Zhang, Chen Zhang, Zuozhu Liu, Hongwei Wang, and Haizhou Li. 2024 · 2024
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Deepseekmath: Pushing the limits of mathematical reasoning in open language models
Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Jun-Mei Song, Mingchuan Zhang, Y. K. Li, Yu Wu, and Daya Guo. 2024 · 2024
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Cited alongside, same era.
Lima: Less is more for alignment
Chunting Zhou, Pengfei Liu, Puxin Xu, Srini Iyer, Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Efrat, Ping Yu, L. Yu, Susan Zhang, Gargi Ghosh, Mike Lewis, Luke Zettlemoyer, and Omer Levy. 2023 · 2023
Cited alongside, same era.
Olora: Orthonormal low-rank adaptation of large language models
Kerim Büyükakyüz. 2024 · 2024
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Foundational Contributors, Ahmed El-Kishky, Daniel Selsam, Francis Song, Giambattista Parascandolo, Hongyu Ren, Hunter Lightman, Hyung Won, Ilge Akkaya, Ilya Sutskever, Jason Wei, Jonathan Gordon, Karl Cobbe, Kevin Yu, Lukasz Kondraciuk, Max Schwarzer, Mostafa Rohaninejad, Noam Brown, Shengjia Zhao, and 189 others. 2024 · 2024
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Adaptive budget allocation for parameter-efficient fine-tuning
Qingru Zhang, Minshuo Chen, Alexander Bukharin, Pengcheng He, Yu Cheng, Weizhu Chen, and Tuo Zhao. 2023a
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Towards adaptive prefix tuning for parameter-efficient language model fine-tuning
Zhenru Zhang, Chuanqi Tan, Haiyang Xu, Chengyu Wang, Jun Huang, and Songfang Huang. 2023b
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Understanding layer significance in llm alignment
Guangyuan Shi, Zexin Lu, Xiaoyu Dong, Wenlong Zhang, Xuanyu Zhang, Yujie Feng, and Xiao-Ming Wu. 2024 · 2024
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Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
DeepSeek-AI, Daya Guo, Dejian Yang, Haowei Zhang, Jun-Mei Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiaoling Bi, Xiaokang Zhang, Xingkai Yu, Yu Wu, Z. F. Wu, Zhibin Gou, Zhihong Shao, Zhuoshu Li, Ziyi Gao, and 179 others. 2025 · 2025
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