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pro vyhledávání: '"Parisi, Aaron T"'
Autor:
Bohnet, Bernd, Nova, Azade, Parisi, Aaron T, Swersky, Kevin, Goshvadi, Katayoon, Dai, Hanjun, Schuurmans, Dale, Fiedel, Noah, Sedghi, Hanie
We seek to elevate the planning capabilities of Large Language Models (LLMs)investigating four main directions. First, we construct a comprehensive benchmark suite encompassing both classical planning domains and natural language scenarios. This suit
Externí odkaz:
http://arxiv.org/abs/2406.13094
Autor:
Bohnet, Bernd, Swersky, Kevin, Liu, Rosanne, Awasthi, Pranjal, Nova, Azade, Snaider, Javier, Sedghi, Hanie, Parisi, Aaron T, Collins, Michael, Lazaridou, Angeliki, Firat, Orhan, Fiedel, Noah
We explore the use of long-context capabilities in large language models to create synthetic reading comprehension data from entire books. Previous efforts to construct such datasets relied on crowd-sourcing, but the emergence of transformers with a
Externí odkaz:
http://arxiv.org/abs/2406.00179
Autor:
Parisi, Aaron Thomas
Publikováno v:
Master's Theses.
The state-of-art model-free reinforcement learning algorithms can generate admissible controls for complicated systems with no prior knowledge of the system dynamics, so long as sufficient (oftentimes millions) of samples are available from the envir