The cross-modal grounding of LLMs has recently garnered significant attention, while grounding them in textual interactions has been less explored. As the first of its kind, the GLAM framework utilises LLMs as agents in interactive text-based games to investigate their grounding capabilities. However, it faces the challenge of low computational efficiency, which hinders further experiments. This paper proposes the use of Lookahead models for action selection, demonstrating through empirical results that the approach can substantially improve training speed, achieving performance gains relative to the size of the action space.