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Large Language Models (LLMs) have become pivotal in addressing reasoning tasks across diverse domains, including arithmetic, commonsense, and symbolic reasoning.
A reinforcement learning approach to job-shop scheduling
Wei Zhang and Thomas G Dietterich · 1995
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Self-improving factory simulation using continuous-time average-reward reinforcement learning
Sridhar Mahadevan, Nicholas Marchalleck, Tapas K Das, and Abhijit Gosavi · 1997
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Optimizing production manufacturing using reinforcement learning
Sridhar Mahadevan and Georgios Theocharous · 1998
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Dynamic job-shop scheduling using reinforcement learning agents
M Emin Aydin and Ercan Öztemel · 2000
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Adaptive reactive job-shop scheduling with reinforcement learning agents
Thomas Gabel and Martin Riedmiller · 2008
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A comparison of priority rules for the job shop scheduling problem under different flow time-and tardiness-related objective functions
Veronique Sels, Nele Gheysen, and Mario Vanhoucke · 2012
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A heuristic approach based on clarke-wright algorithm for open vehicle routing problem
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Pointer networks
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly · 2015
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Proposed heuristic method for solving assignment problems
Samuel Kwame Amponsah, Dominic Otoo, Said Salhi, and Ebenezer Quayson · 2016
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Neural combinatorial optimization with reinforcement learning
Irwan Bello, Hieu Pham, Quoc V Le, Mohammad Norouzi, and Samy Bengio · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Learning heuristics for the tsp by policy gradient
Michel Deudon, Pierre Cournut, Alexandre Lacoste, Yossiri Adulyasak, and Louis-Martin Rousseau · 2018
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Attention, learn to solve routing problems!
Wouter Kool, Herke Van Hoof, and Max Welling · 2018
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Reinforcement learning for solving the vehicle routing problem
Mohammadreza Nazari, Afshin Oroojlooy, Lawrence V Snyder, and Martin Takáč · 2018
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Vehicle routing problem with time windows having stochastic customers demands and stochastic service times: Modelling and solution
Rajeev Kumar Goel, Raman Maini, and Sandhya Bansal · 2019
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Learning scheduling algorithms for data processing clusters
Hongzi Mao, Malte Schwarzkopf, Shaileshh Bojja Venkatakrishnan, Zili Meng, and Mohammad Alizadeh · 2019
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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
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An improved tabu search algorithm for the stochastic vehicle routing problem with soft time windows
Guoming Li and Junhua Li · 2020
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Applying deep learning to the newsvendor problem
Afshin Oroojlooyjadid, Lawrence V Snyder, and Martin Takáč · 2020
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Rlscheduler: an automated hpc batch job scheduler using reinforcement learning
Di Zhang, Dong Dai, Youbiao He, Forrest Sheng Bao, and Bing Xie · 2020
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Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, and Noah Fiedel · 2023
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Or-tools, 2023
Google · 2023
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On the study of curriculum learning for inferring dispatching policies on the job shop scheduling
Zangir Iklassov, Dmitrii Medvedev, Ruben Solozabal Ochoa de Retana, and Martin Takác · 2023
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Reinforcement learning approach to stochastic vehicle routing problem with correlated demands
Zangir Iklassov, Ikboljon Sobirov, Ruben Solozabal, and Martin Takác · 2023
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Deepweave: Accelerating job completion time with deep reinforcement learning-based coflow scheduling
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Dynamic job-shop scheduling in smart manufacturing using deep reinforcement learning
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Decomposed prompting: A modular approach for solving complex tasks
Tushar Khot, Harsh Trivedi, Matthew Finlayson, Yao Fu, Kyle Richardson, Peter Clark, and Ashish Sabharwal · 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
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F. Christiano, Jan Leike, and Ryan Lowe · 2022
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Lamda: Language models for dialog applications
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Self-consistency improves chain of thought reasoning in language models
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Self-refine: Iterative refinement with self-feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Sean Welleck, Bodhisattwa Prasad Majumder, Shashank Gupta, Amir Yazdanbakhsh, and Peter Clark · 2023
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The parallelism tradeoff: Limitations of log-precision transformers
William Merrill and Ashish Sabharwal · 2023
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Challenging big-bench tasks and whether chain-of-thought can solve them
Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V. Le, Ed Chi, Denny Zhou, and Jason Wei · 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 Narasimhan · 2023
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Automatic chain of thought prompting in large language models
Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola · 2023
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Least-to-most prompting enables complex reasoning in large language models
Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V. Le, and Ed H. Chi · 2023
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Evolution of heuristics: Towards efficient automatic algorithm design using large language mode, 2024
Fei Liu, Xialiang Tong, Mingxuan Yuan, Xi Lin, Fu Luo, Zhenkun Wang, Zhichao Lu, and Qingfu Zhang · 2024
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Exploring combinatorial problem solving with large language models: A case study on the travelling salesman problem using gpt-3.5 turbo, 2024
Mahmoud Masoud, Ahmed Abdelhay, and Mohammed Elhenawy · 2024
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Large language models as optimizers, 2024
Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V. Le, Denny Zhou, and Xinyun Chen · 2024
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Prompting with divide-and-conquer program makes large language models discerning to hallucination and deception, 2024
Yizhou Zhang, Lun Du, Defu Cao, Qiang Fu, and Yan Liu · 2024
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