  Following the success of the in-context learning paradigm in large-scale
language and computer vision models, the recently emerging field of in-context
reinforcement learning is experiencing a rapid growth. However, its development
has been held back by the lack of challenging benchmarks, as all the
experiments have been carried out in simple environments and on small-scale
datasets. We present \textbf{XLand-100B}, a large-scale dataset for in-context
reinforcement learning based on the XLand-MiniGrid environment, as a first step
to alleviate this problem. It contains complete learning histories for nearly
$30,000$ different tasks, covering $100$B transitions and $2.5$B episodes. It
took $50,000$ GPU hours to collect the dataset, which is beyond the reach of
most academic labs. Along with the dataset, we provide the utilities to
reproduce or expand it even further. With this substantial effort, we aim to
democratize research in the rapidly growing field of in-context reinforcement
learning and provide a solid foundation for further scaling. The code is
open-source and available under Apache 2.0 licence at
https://github.com/dunno-lab/xland-minigrid-datasets.
