Fetching the paper…
Reading the bibliography…
Recent studies empirically reveal that large reasoning models (LRMs) can automatically allocate more reasoning strengths (i.e., the number of reasoning tokens) for harder problems, exhibiting difficulty-awareness for better task performance.
Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
Earlier work this paper cites.
Understanding intermediate layers using linear classifier probes
Guillaume Alain and Yoshua Bengio · 2017
Earlier work this paper cites.
Lasso regression
Jonas Ranstam and Jonathan A Cook · 2018
Earlier work this paper cites.
Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared 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 M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher 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
Earlier work this paper cites.
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
Earlier work this paper cites.
Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Pondé 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, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Joshua Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba · 2021
Earlier work this paper cites.
Program synthesis with large language models
Jacob Austin, Augustus Odena, Maxwell I. Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie J. Cai, Michael Terry, Quoc V. Le, and Charles Sutton · 2021
Earlier work this paper cites.
Language models represent space and time
Wes Gurnee and Max Tegmark · 2023
Earlier work this paper cites.
Emergent world representations: Exploring a sequence model trained on a synthetic task
Kenneth Li, Aspen K. Hopkins, David Bau, Fernanda B. Viégas, Hanspeter Pfister, and Martin Wattenberg · 2023
Earlier work this paper cites.
Efficient memory management for large language model serving with pagedattention
Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica · 2023
Earlier work this paper cites.
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
Earlier work this paper cites.
GPQA: A graduate-level google-proof q&a benchmark
David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, and Samuel R. Bowman · 2024
Earlier work this paper cites.
Do NOT think that much for 2+3=? on the overthinking of o1-like llms
Xingyu Chen, Jiahao Xu, Tian Liang, Zhiwei He, Jianhui Pang, Dian Yu, Linfeng Song, Qiuzhi Liu, Mengfei Zhou, Zhuosheng Zhang, Rui Wang, Zhaopeng Tu, Haitao Mi, and Dong Yu · 2024
Earlier work this paper cites.
Following length constraints in instructions
Weizhe Yuan, Ilia Kulikov, Ping Yu, Kyunghyun Cho, Sainbayar Sukhbaatar, Jason Weston, and Jing Xu · 2024
Earlier work this paper cites.
Scaling LLM test-time compute optimally can be more effective than scaling model parameters
Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar · 2024
Earlier work this paper cites.
Steering llama 2 via contrastive activation addition
Nina Rimsky, Nick Gabrieli, Julian Schulz, Meg Tong, Evan Hubinger, and Alexander Matt Turner · 2024
Earlier work this paper cites.
Do language models plan ahead for future tokens?
Wilson Wu, John X Morris, and Lionel Levine · 2024
Cited alongside, same era.
Unlocking the future: Exploring look-ahead planning mechanistic interpretability in large language models
Tianyi Men, Pengfei Cao, Zhuoran Jin, Yubo Chen, Kang Liu, and Jun Zhao · 2024
Cited alongside, same era.
The linear representation hypothesis and the geometry of large language models
Kiho Park, Yo Joong Choe, and Victor Veitch · 2024
Cited alongside, same era.
Refusal in language models is mediated by a single direction
Andy Arditi, Oscar Obeso, Aaquib Syed, Daniel Paleka, Nina Panickssery, Wes Gurnee, and Neel Nanda · 2024
Cited alongside, same era.
Gemma scope: Open sparse autoencoders everywhere all at once on gemma 2
Tom Lieberum, Senthooran Rajamanoharan, Arthur Conmy, Lewis Smith, Nicolas Sonnerat, Vikrant Varma, János Kramár, Anca D. Dragan, Rohin Shah, and Neel Nanda · 2024
Cited alongside, same era.
L1: Controlling how long a reasoning model thinks with reinforcement learning
Pranjal Aggarwal and Sean Welleck · 2025
Closest in time.
Precise length control for large language models
Bradley Butcher, Michael O’Keefe, and James Titchener · 2025
Closest in time.
Thinkedit: Interpretable weight editing to mitigate overly short thinking in reasoning models
Chung-En Sun, Ge Yan, and Tsui-Wei Weng · 2025
Closest in time.
Towards understanding distilled reasoning models: A representational approach
David D Baek and Max Tegmark · 2025
Closest in time.
DAST: difficulty-adaptive slow-thinking for large reasoning models
Yi Shen, Jian Zhang, Jieyun Huang, Shuming Shi, Wenjing Zhang, Jiangze Yan, Ning Wang, Kai Wang, and Shiguo Lian · 2025
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Diff-in-means concept editing is worst-case optimal, May 2024
Sam Marks and Max Tegmark · 2024
Cited alongside, same era.
American invitational mathematics examination (aime), February 2024
Mathematical Association of America · 2024
Cited alongside, same era.
Olympiadbench: A challenging benchmark for promoting AGI with olympiad-level bilingual multimodal scientific problems
Chaoqun He, Renjie Luo, Yuzhuo Bai, Shengding Hu, Zhen Leng Thai, Junhao Shen, Jinyi Hu, Xu Han, Yujie Huang, Yuxiang Zhang, Jie Liu, Lei Qi, Zhiyuan Liu, and Maosong Sun · 2024
Cited alongside, same era.
Length-controlled alpacaeval: A simple way to debias automatic evaluators
Yann Dubois, Balázs Galambosi, Percy Liang, and Tatsunori B. Hashimoto · 2024
Cited alongside, same era.
An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jingren Zhou, Junyang Lin, Kai Dang, Keming Lu, Keqin Bao, Kexin Yang, Le Yu, Mei Li, Mingfeng Xue, Pei Zhang, Qin Zhu, Rui Men, Runji Lin, Tianhao Li, Tingyu Xia, Xingzhang Ren, Xuancheng Ren, Yang Fan, Yang Su, Yichang Zhang, Yu Wan, Yuqiong Liu, Zeyu Cui, Zhenru Zhang, and Zihan Qiu · 2024
Cited alongside, same era.
Qwq-32b: Embracing the power of reinforcement learning, March 2025
Qwen Team · 2025
Cited alongside, same era.
Niklas Muennighoff, Zitong Yang, Weijia Shi, Xiang Lisa Li, Li Fei-Fei, Hannaneh Hajishirzi, Luke Zettlemoyer, Percy Liang, Emmanuel Candès, and Tatsunori Hashimoto · 2025
Cited alongside, same era.
Closest in time.
Zhiyuan Zeng, Qinyuan Cheng, Zhangyue Yin, Yunhua Zhou, and Xipeng Qiu · 2025
Closest in time.
From system 1 to system 2: A survey of reasoning large language models
Zhong-Zhi Li, Duzhen Zhang, Ming-Liang Zhang, Jiaxin Zhang, Zengyan Liu, Yuxuan Yao, Haotian Xu, Junhao Zheng, Pei-Jie Wang, Xiuyi Chen, Yingying Zhang, Fei Yin, Jiahua Dong, Zhijiang Guo, Le Song, and Cheng-Lin Liu · 2025
Closest in time.
On the biology of a large language model, March 2025
Anthropic · 2025
Closest in time.
Emergent response planning in LLM
Zhichen Dong, Zhanhui Zhou, Zhixuan Liu, Chao Yang, and Chaochao Lu · 2025
Closest in time.
The geometry of refusal in large language models: Concept cones and representational independence
Tom Wollschläger, Jannes Elstner, Simon Geisler, Vincent Cohen-Addad, Stephan Günnemann, and Johannes Gasteiger · 2025
Closest in time.
Chenlu Ding, Jiancan Wu, Leheng Sheng, Fan Zhang, Yancheng Yuan, Xiang Wang, and Xiangnan He · 2025
Closest in time.
The reasoning-memorization interplay in language models is mediated by a single direction
Yihuai Hong, Dian Zhou, Meng Cao, Lei Yu, and Zhijing Jin · 2025
Closest in time.
Xinyu Tang, Xiaolei Wang, Zhihao Lv, Yingqian Min, Wayne Xin Zhao, Binbin Hu, Ziqi Liu, and Zhiqiang Zhang · 2025
Closest in time.
Overthink: Slowdown attacks on reasoning llms
Abhinav Kumar, Jaechul Roh, Ali Naseh, Marzena Karpinska, Mohit Iyyer, Amir Houmansadr, and Eugene Bagdasarian · 2025
Closest in time.
Stop overthinking: A survey on efficient reasoning for large language models
Yang Sui, Yu-Neng Chuang, Guanchu Wang, Jiamu Zhang, Tianyi Zhang, Jiayi Yuan, Hongyi Liu, Andrew Wen, Hanjie Chen, Xia Hu, et al · 2025
Closest in time.
Livecodebench: Holistic and contamination free evaluation of large language models for code
Naman Jain, King Han, Alex Gu, Wen-Ding Li, Fanjia Yan, Tianjun Zhang, Sida Wang, Armando Solar-Lezama, Koushik Sen, and Ion Stoica · 2025
Closest in time.