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Large language models (LLMs) have rapidly progressed into general-purpose agents capable of solving a broad spectrum of tasks.
Approximation Algorithms
Vijay V. Vazirani · 2010
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Thinking, fast and slow
Daniel Kahneman · 2012
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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Measuring mathematical problem solving with the MATH dataset
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt · 2021
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman · 2021
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Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed H. Chi, Quoc V Le, and Denny Zhou · 2022
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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 · 2022
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Multimodal Learning With Transformers: A Survey
Peng Xu, Xiatian Zhu, and David A. Clifton · 2023
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On the planning abilities of large language models - a critical investigation
Karthik Valmeekam, Matthew Marquez, Sarath Sreedharan, and Subbarao Kambhampati · 2023
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Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou · 2023
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Let’s sample step by step: Adaptive-consistency for efficient reasoning and coding with LLMs
Pranjal Aggarwal, Aman Madaan, Yiming Yang, and Mausam · 2023
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Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik R Narasimhan · 2023
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Progressive-hint prompting improves reasoning in large language models
Chuanyang Zheng, Zhengying Liu, Enze Xie, Zhenguo Li, and Yu Li · 2023
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OPTQ: Accurate quantization for generative pre-trained transformers
Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh · 2023
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A survey on large language models for code generation
Juyong Jiang, Fan Wang, Jiasi Shen, Sungju Kim, and Sunghun Kim · 2024
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Large language models for mathematical reasoning: Progresses and challenges
Janice Ahn, Rishu Verma, Renze Lou, Di Liu, Rui Zhang, and Wenpeng Yin · 2024
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Evaluating spatial understanding of large language models
Yutaro Yamada, Yihan Bao, Andrew Kyle Lampinen, Jungo Kasai, and Ilker Yildirim · 2024
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OpenAI · 2024
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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
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Scaling llm test-time compute optimally can be more effective than scaling model parameters
Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar · 2024
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Claude 3.7 sonnet system card, 2024
Anthropic · 2024
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The benefits of a concise chain of thought on problem-solving in large language models
Matthew Renze and Erhan Guven · 2024
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Training language models to self-correct via reinforcement learning
Aviral Kumar, Vincent Zhuang, Rishabh Agarwal, Yi Su, John D Co-Reyes, Avi Singh, Kate Baumli, Shariq Iqbal, Colton Bishop, Rebecca Roelofs, Lei M Zhang, Kay McKinney, Disha Shrivastava, Cosmin Paduraru, George Tucker, Doina Precup, Feryal Behbahani, and Aleksandra Faust · 2024
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Escape sky-high cost: Early-stopping self-consistency for multi-step reasoning
Yiwei Li, Peiwen Yuan, Shaoxiong Feng, Boyuan Pan, Xinglin Wang, Bin Sun, Heda Wang, and Kan Li · 2024
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Path-Consistency: Prefix Enhancement for Efficient Inference in LLM
Jiace Zhu, Yingtao Shen, Jie Zhao, and An Zou · 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
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Efficiently Serving LLM Reasoning Programs with Certaindex
Yichao Fu, Junda Chen, Siqi Zhu, Zheyu Fu, Zhongdongming Dai, Aurick Qiao, and Hao Zhang · 2024
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Fast best-of-n decoding via speculative rejection
Hanshi Sun, Momin Haider, Ruiqi Zhang, Huitao Yang, Jiahao Qiu, Ming Yin, Mengdi Wang, Peter Bartlett, and Andrea Zanette · 2024
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Reasoning Aware Self-Consistency: Leveraging Reasoning Paths for Efficient LLM Sampling
Guangya Wan, Yuqi Wu, Jie Chen, and Sheng Li · 2024
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Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation
Rohin Manvi, Anikait Singh, and Stefano Ermon · 2024
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Distilling system 2 into system 1
Ping Yu, Jing Xu, Jason Weston, and Ilia Kulikov · 2024
Cited alongside, same era.
Training large language models to reason in a continuous latent space
Shibo Hao, Sainbayar Sukhbaatar, DiJia Su, Xian Li, Zhiting Hu, Jason Weston, and Yuandong Tian · 2024
Cited alongside, same era.
Chain of preference optimization: Improving chain-of-thought reasoning in llms
Xuan Zhang, Chao Du, Tianyu Pang, Qian Liu, Wei Gao, and Min Lin · 2024
Cited alongside, same era.
Let’s verify step by step
Hunter Lightman, Vineet Kosaraju, Yuri Burda, Harrison Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe · 2024
Cited alongside, same era.
Keep the cost down: A review on methods to optimize LLM’s KV-cache consumption
Shi Luohe, Hongyi Zhang, Yao Yao, Zuchao Li, and hai zhao · 2024
Cited alongside, same era.
Dynamic parallel tree search for efficient llm reasoning
Yifu Ding, Wentao Jiang, Shunyu Liu, Yongcheng Jing, Jinyang Guo, Yingjie Wang, Jing Zhang, Zengmao Wang, Ziwei Liu, Bo Du, Xianglong Liu, and Dacheng Tao · 2025
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Sampling-Efficient Test-Time Scaling: Self-Estimating the Best-of-N Sampling in Early Decoding
Yiming Wang, Pei Zhang, Siyuan Huang, Baosong Yang, Zhuosheng Zhang, Fei Huang, and Rui Wang · 2025
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Learning how hard to think: Input-adaptive allocation of LM computation
Mehul Damani, Idan Shenfeld, Andi Peng, Andreea Bobu, and Jacob Andreas · 2025
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Efficient Test-Time Scaling via Self-Calibration
Chengsong Huang, Langlin Huang, Jixuan Leng, Jiacheng Liu, and Jiaxin Huang · 2025
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Think more, hallucinate less: Mitigating hallucinations via dual process of fast and slow thinking
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Efficient large language models: A survey
Zhongwei Wan, Xin Wang, Che Liu, Samiul Alam, Yu Zheng, Jiachen Liu, Zhongnan Qu, Shen Yan, Yi Zhu, Quanlu Zhang, Mosharaf Chowdhury, and Mi Zhang · 2024
Cited alongside, same era.
Universal self-consistency for large language models
Xinyun Chen, Renat Aksitov, Uri Alon, Jie Ren, Kefan Xiao, Pengcheng Yin, Sushant Prakash, Charles Sutton, Xuezhi Wang, and Denny Zhou · 2024
Cited alongside, same era.
The impact of reasoning step length on large language models
Mingyu Jin, Qinkai Yu, Dong Shu, Haiyan Zhao, Wenyue Hua, Yanda Meng, Yongfeng Zhang, and Mengnan Du · 2024
Cited alongside, same era.
Can language models learn to skip steps?
Tengxiao Liu, Qipeng Guo, Xiangkun Hu, Cheng Jiayang, Yue Zhang, Xipeng Qiu, and Zheng Zhang · 2024
Cited alongside, same era.
Branch-solve-merge improves large language model evaluation and generation
Swarnadeep Saha, Omer Levy, Asli Celikyilmaz, Mohit Bansal, Jason Weston, and Xian Li · 2024
Cited alongside, same era.
Compressed chain of thought: Efficient reasoning through dense representations
Jeffrey Cheng and Benjamin Van Durme · 2024
Cited alongside, same era.
Introducing deepseek-v3, 2024
DeepSeek · 2024
Cited alongside, same era.
Xiaoxue Cheng, Junyi Li, Wayne Xin Zhao, and Ji-Rong Wen · 2025
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Learning adaptive parallel reasoning with language models
Jiayi Pan, Xiuyu Li, Long Lian, Charlie Snell, Yifei Zhou, Adam Yala, Trevor Darrell, Kurt Keutzer, and Alane Suhr · 2025
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Sketch-of-thought: Efficient llm reasoning with adaptive cognitive-inspired sketching
Simon A. Aytes, Jinheon Baek, and Sung Ju Hwang · 2025
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Token-budget-aware llm reasoning, 2025
Tingxu Han, Zhenting Wang, Chunrong Fang, Shiyu Zhao, Shiqing Ma, and Zhenyu Chen · 2025
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Dualformer: Controllable fast and slow thinking by learning with randomized reasoning traces
DiJia Su, Sainbayar Sukhbaatar, Michael Rabbat, Yuandong Tian, and Qinqing Zheng · 2025
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Self-training elicits concise reasoning in large language models
Tergel Munkhbat, Namgyu Ho, Seo Hyun Kim, Yongjin Yang, Yujin Kim, and Se-Young Yun · 2025
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Retro-search: Exploring untaken paths for deeper and efficient reasoning
Ximing Lu, Seungju Han, David Acuna, Hyunwoo Kim, Jaehun Jung, Shrimai Prabhumoye, Niklas Muennighoff, Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro, and Yejin Choi · 2025
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O1-pruner: Length-harmonizing fine-tuning for o1-like reasoning pruning
Haotian Luo, Li Shen, Haiying He, Yibo Wang, Shiwei Liu, Wei Li, Naiqiang Tan, Xiaochun Cao, and Dacheng Tao · 2025
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Refining answer distributions for improved large language model reasoning
Soumyasundar Pal, Didier Chételat, Yingxue Zhang, and Mark Coates · 2025
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Art of problem solving, 2025
AIME · 2025
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SFT memorizes, RL generalizes: A comparative study of foundation model post-training
Tianzhe Chu, Yuexiang Zhai, Jihan Yang, Shengbang Tong, Saining Xie, Sergey Levine, and Yi Ma · 2025
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Let llms break free from overthinking via self-braking tuning
Haoran Zhao, Yuchen Yan, Yongliang Shen, Haolei Xu, Wenqi Zhang, Kaitao Song, Jian Shao, Weiming Lu, Jun Xiao, and Yueting Zhuang · 2025
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Inftythink: Breaking the length limits of long-context reasoning in large language models
Yuchen Yan, Yongliang Shen, Yang Liu, Jin Jiang, Mengdi Zhang, Jian Shao, and Yueting Zhuang · 2025
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Training language models to reason efficiently
Daman Arora and Andrea Zanette · 2025
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Vapo: Efficient and reliable reinforcement learning for advanced reasoning tasks
Yu Yue, Yufeng Yuan, Qiying Yu, Xiaochen Zuo, Ruofei Zhu, Wenyuan Xu, Jiaze Chen, Chengyi Wang, TianTian Fan, Zhengyin Du, Xiangpeng Wei, Xiangyu Yu, Gaohong Liu, Juncai Liu, Lingjun Liu, Haibin Lin, Zhiqi Lin, Bole Ma, Chi Zhang, Mofan Zhang, Wang Zhang, Hang Zhu, Ru Zhang, Xin Liu, Mingxuan Wang, Yonghui Wu, and Lin Yan · 2025
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Concise reasoning via reinforcement learning
Mehdi Fatemi, Banafsheh Rafiee, Mingjie Tang, and Kartik Talamadupula · 2025
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Think only when you need with large hybrid-reasoning models
Lingjie Jiang, Xun Wu, Shaohan Huang, Qingxiu Dong, Zewen Chi, Li Dong, Xingxing Zhang, Tengchao Lv, Lei Cui, and Furu Wei · 2025
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Demystifying long chain-of-thought reasoning in LLMs
Edward Yeo, Yuxuan Tong, Xinyao Niu, Graham Neubig, and Xiang Yue · 2025
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Large language diffusion models
Shen Nie, Fengqi Zhu, Zebin You, Xiaolu Zhang, Jingyang Ou, Jun Hu, Jun Zhou, Yankai Lin, Ji-Rong Wen, and Chongxuan Li · 2025
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Video-r1: Reinforcing video reasoning in mllms
Kaituo Feng, Kaixiong Gong, Bohao Li, Zonghao Guo, Yibing Wang, Tianshuo Peng, Benyou Wang, and Xiangyu Yue · 2025
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R1-zero’s" aha moment" in visual reasoning on a 2b non-sft model
Hengguang Zhou, Xirui Li, Ruochen Wang, Minhao Cheng, Tianyi Zhou, and Cho-Jui Hsieh · 2025
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Fast-slow thinking for large vision-language model reasoning
Wenyi Xiao, Leilei Gan, Weilong Dai, Wanggui He, Ziwei Huang, Haoyuan Li, Fangxun Shu, Zhelun Yu, Peng Zhang, Hao Jiang, et al · 2025
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Skywork r1v: Pioneering multimodal reasoning with chain-of-thought
Yi Peng, Chris, Xiaokun Wang, Yichen Wei, Jiangbo Pei, Weijie Qiu, Ai Jian, Yunzhuo Hao, Jiachun Pan, Tianyidan Xie, Li Ge, Rongxian Zhuang, Xuchen Song, Yang Liu, and Yahui Zhou · 2025
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Qwen3: Think deeper, act faster, 2025
Alibaba · 2025
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Haicheng Liao, Hanlin Kong, Bonan Wang, Chengyue Wang, Wang Ye, Zhengbing He, Chengzhong Xu, and Zhenning Li · 2025
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Distilling multi-modal large language models for autonomous driving
Deepti Hegde, Rajeev Yasarla, Hong Cai, Shizhong Han, Apratim Bhattacharyya, Shweta Mahajan, Litian Liu, Risheek Garrepalli, Vishal M. Patel, and Fatih Porikli · 2025
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Fit and prune: Fast and training-free visual token pruning for multi-modal large language models
Weihao Ye, Qiong Wu, Wenhao Lin, and Yiyi Zhou · 2025
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