Fetching the paper…
Reading the bibliography…
Discovering efficient algorithms for solving complex problems has been an outstanding challenge in mathematics and computer science, requiring substantial human expertise over the years.
Gradient theory of optimal flight paths
Henry J Kelley · 1960
Earlier work this paper cites.
The Theory of Error-correcting Codes
F.J. MacWilliams and N.J.A. Sloane · 1977
Earlier work this paper cites.
Optimization by simulated annealing
S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi · 1983
Earlier work this paper cites.
Approximation algorithms for bin-packing—an updated survey
Edward G Coffman Jr, Michael R Garey, and David S Johnson · 1984
Earlier work this paper cites.
Learning representations by back-propagating errors
David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams · 1986
Earlier work this paper cites.
Genetic algorithms and machine learning
David E. Goldberg and John H. Holland · 1988
Earlier work this paper cites.
Distributed genetic algorithms for function optimization
Reiko Tanese · 1989
Earlier work this paper cites.
Or-library: distributing test problems by electronic mail
John E Beasley · 1990
Earlier work this paper cites.
Lower bounds and reduction procedures for the bin packing problem
Silvano Martello and Paolo Toth · 1990
Earlier work this paper cites.
Star: Bootstrapping reasoning with reasoning
Eric Zelikman, Yuhuai Wu, Jesse Mu, and Noah Goodman · 1990
Earlier work this paper cites.
The traveling salesman problem
Michael Jünger, Gerhard Reinelt, and Giovanni Rinaldi · 1995
Earlier work this paper cites.
A survey of parallel genetic algorithms
Erick Cantú-Paz et al · 1998
Earlier work this paper cites.
Guided local search and its application to the traveling salesman problem
Christos Voudouris and Edward Tsang · 1999
Earlier work this paper cites.
The traveling salesman problem and its variations , volume 12
Gregory Gutin and Abraham P Punnen · 2006
Earlier work this paper cites.
Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Earlier work this paper cites.
Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver · 2015
Earlier work this paper cites.
Pointer networks
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly · 2015
Earlier work this paper cites.
Neural combinatorial optimization with reinforcement learning
Irwan Bello, Hieu Pham, Quoc V Le, Mohammad Norouzi, and Samy Bengio · 2016
Earlier work this paper cites.
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, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis · 2016
Earlier work this paper cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Earlier work this paper cites.
Guided local search., 2018
Abdullah Alsheddy, Christos Voudouris, Edward PK Tsang, and Ahmad Alhindi · 2018
Earlier work this paper cites.
Attention, learn to solve routing problems!
Wouter Kool, Herke Van Hoof, and Max Welling · 2018
Earlier work this paper cites.
Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
Earlier work this paper cites.
Knowledge-guided local search for the vehicle routing problem
Florian Arnold and Kenneth Sörensen · 2019
Earlier work this paper cites.
Dota 2 with large scale deep reinforcement learning
Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemysław Dębiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq Hashme, Chris Hesse, et al · 2019
Earlier work this paper cites.
Learning to perform local rewriting for combinatorial optimization
Xinyun Chen and Yuandong Tian · 2019
Cited alongside, same era.
elkai: A python library for solving travelling salesman problems, 2019
F. Dimitrovski · 2019
Cited alongside, same era.
New applications of the polynomial method: the cap set conjecture and beyond
Joshua Grochow · 2019
Cited alongside, same era.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi · 2019
Cited alongside, same era.
An efficient graph convolutional network technique for the travelling salesman problem
Chaitanya K Joshi, Thomas Laurent, and Xavier Bresson · 2019
Cited alongside, same era.
Reinforced self-training (rest) for language modeling
Caglar Gulcehre, Tom Le Paine, Srivatsan Srinivasan, Ksenia Konyushkova, Lotte Weerts, Abhishek Sharma, Aditya Siddhant, Alex Ahern, Miaosen Wang, Chenjie Gu, et al · 2023
Later among the works it cites.
Connecting large language models with evolutionary algorithms yields powerful prompt optimizers
Qingyan Guo, Rui Wang, Junliang Guo, Bei Li, Kaitao Song, Xu Tan, Guoqing Liu, Jiang Bian, Yujiu Yang, Tsinghua University, and Microsoft Research · 2023
Later among the works it cites.
Understanding the effects of rlhf on llm generalisation and diversity
Robert Kirk, Ishita Mediratta, Christoforos Nalmpantis, Jelena Luketina, Eric Hambro, Edward Grefenstette, and Roberta Raileanu · 2023
Later among the works it cites.
Evolution through large models
Joel Lehman, Jonathan Gordon, Shawn Jain, Kamal Ndousse, Cathy Yeh, and Kenneth O Stanley · 2023
Later among the works it cites.
Neural combinatorial optimization with heavy decoder: Toward large scale generalization
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
An overview of gradient descent optimization algorithms
Sebastian Ruder · 2019
Cited alongside, same era.
The bitter lesson, March 2019
Rich Sutton · 2019
Cited alongside, same era.
Grandmaster level in starcraft ii using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al · 2019
Cited alongside, same era.
Rl unplugged: A collection of benchmarks for offline reinforcement learning
Çaglar Gülçehre, Ziyu Wang, Alexander Novikov, Thomas Paine, Sergio Gómez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel J Mankowitz, Cosmin Paduraru, et al · 2020
Cited alongside, same era.
Neural large neighborhood search for the capacitated vehicle routing problem
André Hottung and Kevin Tierney · 2020
Cited alongside, same era.
Pomo: Policy optimization with multiple optima for reinforcement learning
Yeong-Dae Kwon, Jinho Choo, Byoungjip Kim, Iljoo Yoon, Youngjune Gwon, and Seungjai Min · 2020
Cited alongside, same era.
Bandit algorithms
Tor Lattimore and Csaba Szepesvári · 2020
Cited alongside, same era.
Fu Luo, Xi Lin, Fei Liu, Qingfu Zhang, and Zhenkun Wang · 2023
Later among the works it cites.
Eureka: Human-level reward design via coding large language models
Yecheng Jason Ma, William Liang, Guanzhi Wang, De-An Huang, Osbert Bastani, Dinesh Jayaraman, Yuke Zhu, Linxi Fan, and Anima Anandkumar · 2023
Later among the works it cites.
Beyond human data: Scaling self-training for problem-solving with language models
Avi Singh, John D Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Peter J Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron Parisi, et al · 2023
Later among the works it cites.
Beyond reverse kl: Generalizing direct preference optimization with diverse divergence constraints
Chaoqi Wang, Yibo Jiang, Chenghao Yang, Han Liu, and Yuxin Chen · 2023
Later among the works it cites.
Phi-3 technical report: A highly capable language model locally on your phone
Marah Abdin, Jyoti Aneja, Hany Awadalla, Ahmed Awadallah, Ammar Ahmad Awan, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Jianmin Bao, Harkirat Behl, et al · 2024
Later among the works it cites.
Human alignment of large language models through online preference optimisation
Daniele Calandriello, Daniel Guo, Remi Munos, Mark Rowland, Yunhao Tang, Bernardo Avila Pires, Pierre Harvey Richemond, Charline Le Lan, Michal Valko, Tianqi Liu, et al · 2024
Later among the works it cites.
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al · 2024
Later among the works it cites.
Granite 3.0 language models, October 2024
IBM Granite Team · 2024
Later among the works it cites.
Can large language models invent algorithms to improve themselves?
Yoichi Ishibashi, Taro Yano, and Masafumi Oyamada · 2024
Later among the works it cites.
Evolution of heuristics: Towards efficient automatic algorithm design using large language model
Fei Liu, Xialiang Tong, Mingxuan Yuan, Xi Lin, Fu Luo, Zhenkun Wang, Zhichao Lu, and Qingfu Zhang · 2024
Later among the works it cites.
Discovering preference optimization algorithms with and for large language models
Chris Lu, Samuel Holt, Claudio Fanconi, Alex Chan, Jakob Foerster, Mihaela van der Schaar, and Robert Lange · 2024
Later among the works it cites.
Iterative reasoning preference optimization
Richard Yuanzhe Pang, Weizhe Yuan, He He, Kyunghyun Cho, Sainbayar Sukhbaatar, and Jason Weston · 2024
Later among the works it cites.
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
Later among the works it cites.
Mathematical discoveries from program search with large language models
B. Romera-Paredes, M. Barekatain, A. Novikov, et al · 2024
Later among the works it cites.
Llm-sr: Scientific equation discovery via programming with large language models
Parshin Shojaee, Kazem Meidani, Shashank Gupta, Amir Barati Farimani, and Chandan K Reddy · 2024
Later among the works it cites.
Ai models collapse when trained on recursively generated data
I. Shumailov, Z. Shumaylov, Y. Zhao, et al · 2024
Later among the works it cites.
Neuralgls: learning to guide local search with graph convolutional network for the traveling salesman problem
Jingyan Sui, Shizhe Ding, Boyang Xia, Ruizhi Liu, and Dongbo Bu · 2024
Later among the works it cites.
Amplifying human performance in combinatorial competitive programming
Petar Veličković, Alex Vitvitskyi, Larisa Markeeva, Borja Ibarz, Lars Buesing, Matej Balog, and Alexander Novikov · 2024
Later among the works it cites.
Large language models as optimizers
Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V Le, Denny Zhou, and Xinyun Chen · 2024
Later among the works it cites.
Reevo: Large language models as hyper-heuristics with reflective evolution
Haoran Ye, Jiarui Wang, Zhiguang Cao, Federico Berto, Chuanbo Hua, Haeyeon Kim, Jinkyoo Park, and Guojie Song · 2024
Later among the works it cites.
Neobert: A next-generation bert
Lola Le Breton, Quentin Fournier, Mariam El Mezouar, and Sarath Chandar · 2025
Closest in time.
Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning, 2025
DeepSeek-AI, Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, Xiaokang Zhang, Xingkai Yu, Yu Wu, Z. F. Wu, Zhibin Gou, Zhihong Shao, Zhuoshu Li, Ziyi Gao, Aixin Liu, Bing Xue, Bingxuan Wang, Bochao Wu, Bei Feng, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, Damai Dai, Deli Chen, Dongjie Ji, Erhang Li, Fangyun Lin, Fucong Dai, Fuli Luo, Guangbo Hao, Guanting Chen, Guowei Li, H. Zhang, Han Bao, Hanwei Xu, Haocheng Wang, Honghui Ding, Huajian Xin, Huazuo Gao, Hui Qu, Hui Li, Jianzhong Guo, Jiashi Li, Jiawei Wang, Jingchang Chen, Jingyang Yuan, Junjie Qiu, Junlong Li, J. L. Cai, Jiaqi Ni, Jian Liang, Jin Chen, Kai Dong, Kai Hu, Kaige Gao, Kang Guan, Kexin Huang, Kuai Yu, Lean Wang, Lecong Zhang, Liang Zhao, Litong Wang, Liyue Zhang, Lei Xu, Leyi Xia, Mingchuan Zhang, Minghua Zhang, Minghui Tang, Meng Li, Miaojun Wang, Mingming Li, Ning Tian, Panpan Huang, Peng Zhang, Qiancheng Wang, Qinyu Chen, Qiushi Du, Ruiqi Ge, Ruisong Zhang, Ruizhe Pan, Runji Wang, R. J. Chen, R. L. Jin, Ruyi Chen, Shanghao Lu, Shangyan Zhou, Shanhuang Chen, Shengfeng Ye, Shiyu Wang, Shuiping Yu, Shunfeng Zhou, Shuting Pan, S. S. Li, Shuang Zhou, Shaoqing Wu, Shengfeng Ye, Tao Yun, Tian Pei, Tianyu Sun, T. Wang, Wangding Zeng, Wanjia Zhao, Wen Liu, Wenfeng Liang, Wenjun Gao, Wenqin Yu, Wentao Zhang, W. L. Xiao, Wei An, Xiaodong Liu, Xiaohan Wang, Xiaokang Chen, Xiaotao Nie, Xin Cheng, Xin Liu, Xin Xie, Xingchao Liu, Xinyu Yang, Xinyuan Li, Xuecheng Su, Xuheng Lin, X. Q. Li, Xiangyue Jin, Xiaojin Shen, Xiaosha Chen, Xiaowen Sun, Xiaoxiang Wang, Xinnan Song, Xinyi Zhou, Xianzu Wang, Xinxia Shan, Y. K. Li, Y. Q. Wang, Y. X. Wei, Yang Zhang, Yanhong Xu, Yao Li, Yao Zhao, Yaofeng Sun, Yaohui Wang, Yi Yu, Yichao Zhang, Yifan Shi, Yiliang Xiong, Ying He, Yishi Piao, Yisong Wang, Yixuan Tan, Yiyang Ma, Yiyuan Liu, Yongqiang Guo, Yuan Ou, Yuduan Wang, Yue Gong, Yuheng Zou, Yujia He, Yunfan Xiong, Yuxiang Luo, Yuxiang You, Yuxuan Liu, Yuyang Zhou, Y. X. Zhu, Yanhong Xu, Yanping Huang, Yaohui Li, Yi Zheng, Yuchen Zhu, Yunxian Ma, Ying Tang, Yukun Zha, Yuting Yan, Z. Z. Ren, Zehui Ren, Zhangli Sha, Zhe Fu, Zhean Xu, Zhenda Xie, Zhengyan Zhang, Zhewen Hao, Zhicheng Ma, Zhigang Yan, Zhiyu Wu, Zihui Gu, Zijia Zhu, Zijun Liu, Zilin Li, Ziwei Xie, Ziyang Song, Zizheng Pan, Zhen Huang, Zhipeng Xu, Zhongyu Zhang, and Zhen Zhang · 2025
Closest in time.
Text generation inference, 2025
Hugging Face · 2025
Closest in time.