Through providing agents with the capacity to learn sample-efficient and generalisable communications protocols, we may enable them to more effectively cooperate in real-world tasks. In this paper, we consider this in the context of discrete decentralised multi-agent reinforcement learning to provide insights into the impact of the often overlooked size of the message set. Within a referential game, we find that over-provisioning the message set size leads to improved sample efficiency, but that these policies tend to maintain a high-degree of redundancy, often utilising multiple messages to refer to each label in the dataset. We hypothesise that the additional redundancy within these converged policies may have implications for generalisation and experiment with methodologies to gradually reduce redundancy while maintaining sample-efficiency. To this end, we propose a linearly-scheduled entropy regulariser which encourages an agent to initially maximise the utilisation of the available messages but, as training progresses, it tries to minimise it. Through this mechanism, we achieve a comparable sample efficiency whilst converging to a model with significantly reduced redundancy and that generalises more effectively to previously unseen data.