We observe that pre-trained large language models (LLMs) are capable of autoregressively
completing complex token sequences--from arbitrary ones procedurally generated by …

Large transformer models pretrained on offline reinforcement learning datasets have
demonstrated remarkable in-context reinforcement learning (ICRL) capabilities, where they …

Large language models (LLMs) demonstrate impressive reasoning abilities, but translating
reasoning into actions in the real world remains challenging. In particular, it remains unclear …

Decision Transformer (DT) is an innovative algorithm leveraging recent advances of the
transformer architecture in reinforcement learning (RL). However, a notable limitation of DT …

Large language models (LLMs) demonstrate impressive reasoning abilities, but translating
reasoning into actions in the real world remains challenging. In particular, it is unclear how …

We investigate the extent to which contemporary Large Language Models (LLMs) can
engage in exploration, a core capability in reinforcement learning and decision making. We …

Large language models (LLMs) have been increasingly employed for (interactive) decision-
making, via the development of LLM-based autonomous agents. Despite their emerging …

We study the\emph {in-context learning}(ICL) ability of a\emph {Linear Transformer
Block}(LTB) that combines a linear attention component and a linear multi-layer perceptron …

This survey presents an overview of methods for learning from video (LfV) in the context of
reinforcement learning (RL) and robotics. We focus on methods capable of scaling to large …

We present XLand-MiniGrid, a suite of tools and grid-world environments for meta-
reinforcement learning research inspired by the diversity and depth of XLand and the …