We study reinforcement learning (RL) agents which can utilize language inputs and efficiently learn on downstream tasks. To investigate this, we propose a new multimodal benchmark -- Text-Conditioned Frostbite -- in which an agent must complete tasks specified by text instructions in the Atari Frostbite environment. We curate and release a dataset of 5M text-labelled transitions for training, and to encourage further research in this direction. On this benchmark, we evaluate Text Decision Transformer (TDT), a transformer directly operating on text, state, and action tokens, and find it improves upon baseline architectures. Furthermore, we evaluate the effect of pretraining, finding unsupervised pretraining can yield improved results in low-data settings.