  Recent works successfully leveraged Large Language Models' (LLM) abilities to
capture abstract knowledge about world's physics to solve decision-making
problems. Yet, the alignment between LLMs' knowledge and the environment can be
wrong and limit functional competence due to lack of grounding. In this paper,
we study an approach (named GLAM) to achieve this alignment through functional
grounding: we consider an agent using an LLM as a policy that is progressively
updated as the agent interacts with the environment, leveraging online
Reinforcement Learning to improve its performance to solve goals. Using an
interactive textual environment designed to study higher-level forms of
functional grounding, and a set of spatial and navigation tasks, we study
several scientific questions: 1) Can LLMs boost sample efficiency for online
learning of various RL tasks? 2) How can it boost different forms of
generalization? 3) What is the impact of online learning? We study these
questions by functionally grounding several variants (size, architecture) of
FLAN-T5.
