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Large Language Models have demonstrated remarkable abilities in reasoning and planning by breaking down complex problems into sequential steps.
Spacetime constraints
Andrew Witkin and Michael Kass · 1988
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Adrian FM Smith and Alan E Gelfand · 1992
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An overview of industrial model predictive control technology
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A tutorial on energy-based learning
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Constrained model predictive control
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ALFWorld: Aligning Text and Embodied Environments for Interactive Learning
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Deterministic policy gradient algorithms
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Beam search strategies for neural machine translation
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Imagination-augmented agents for deep reinforcement learning
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Mastering the game of go without human knowledge
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David Ha and Jürgen Schmidhuber · 2018
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Model-ensemble trust-region policy optimization
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Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning
Anusha Nagabandi, Gregory Kahn, Ronald S Fearing, and Sergey Levine · 2018
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Residual energy-based models for text generation
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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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Program synthesis with large language models
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Evaluating large language models trained on code, 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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Measuring mathematical problem solving with the math dataset
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Neurologic a* esque decoding: Constrained text generation with lookahead heuristics
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Cold decoding: Energy-based constrained text generation with langevin dynamics
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Solving math word problems with process-and outcome-based feedback
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Self-consistency improves chain of thought reasoning in language models
Self-infilling code generation
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Language agent tree search unifies reasoning acting and planning in language models
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Toolchain*: Efficient action space navigation in large language models with a* search
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The pitfalls of next-token prediction
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Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou · 2022
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Chain-of-thought prompting elicits reasoning in large language models
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Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao · 2022
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Reasoning with language model is planning with world model
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Language model decoding as direct metrics optimization
Haozhe Ji, Pei Ke, Hongning Wang, and Minlie Huang · 2023
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Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al · 2023
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Break the sequential dependency of llm inference using lookahead decoding
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Scaling llm test-time compute optimally can be more effective than scaling model parameters
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Empowering large language model agents through action learning
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