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Recent advancements in Large Language Models (LLMs) have revolutionized decision-making by breaking down complex problems into more manageable language sequences referred to as "thoughts".
Finding a shortest solution for the n x n extension of the 15-puzzle is intractable
Daniel Ratner and Manfred Warmuth · 1986
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Markov decision processes
Martin L Puterman · 1990
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On upper-confidence bound policies for switching bandit problems
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Multi-armed bandits with episode context
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The untapped promise of digital mind maps
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Using modern graph analysis techniques on mind maps to help quantify learning
Peter Jamieson · 2012
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Minecraft as a creative tool: A case study
Maria Cipollone, Catherine C Schifter, and Rick A Moffat · 2014
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Solving the rubik’s cube optimally is np-complete
Erik D Demaine, Sarah Eisenstat, and Mikhail Rudoy · 2017
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Deep reinforcement learning: An overview
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models
Priyan Vaithilingam, Tianyi Zhang, and Elena L Glassman · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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Automatic chain of thought prompting in large language models
Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola · 2022
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Graph of thoughts: Solving elaborate problems with large language models
Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Michal Podstawski, Hubert Niewiadomski, Piotr Nyczyk, et al · 2023
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Grounding large language models in interactive environments with online reinforcement learning
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Reflexion: an autonomous agent with dynamic memory and self-reflection
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Progprompt: Generating situated robot task plans using large language models
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Empowering practical root cause analysis by large language models for cloud incidents
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Alphazero-like tree-search can guide large language model decoding and training
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Mathematical capabilities of chatgpt
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Reasoning with language model is planning with world model
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Causal reasoning and large language models: Opening a new frontier for causality
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Reward design with language models
Minae Kwon, Sang Michael Xie, Kalesha Bullard, and Dorsa Sadigh · 2023
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Making ppo even better: Value-guided monte-carlo tree search decoding
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Multi-agent collaboration: Harnessing the power of intelligent llm agents
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Translating natural language to planning goals with large-language models
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Tree of thoughts: Deliberate problem solving with large language models
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Fusing pre-trained language models with multimodal prompts through reinforcement learning
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Multimodal chain-of-thought reasoning in language models
Zhuosheng Zhang, Aston Zhang, Mu Li, Hai Zhao, George Karypis, and Alex Smola · 2023
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