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cs.LG2024
N-Gram Induction Heads for In-Context RL: Improving Stability and Reducing Data Needs
Ilya Zisman, Alexander Nikulin, Viacheslav Sinii +5
In-context learning allows models like transformers to adapt to new tasks from a few examples without updating their weights, a desirable trait for reinforcement learning (RL). How…
cs.LG2024
XLand-100B: A Large-Scale Multi-Task Dataset for In-Context Reinforcement Learning
Alexander Nikulin, Ilya Zisman, Alexey Zemtsov +1
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 ex…