The rapid evolution of large language models (LLMs) has brought remarkable advancements in AI capabilities, but efficiently scaling these models remains a critical challenge. Mixture-of-Experts (MoE) architecture offers a promising solution by scaling model size without drastically increasing training costs. However, existing MoE models often suffer from parameter inefficiency, where a larger MoE model might perform similarly to a smaller standard model. This inefficiency stems from the independent routing decisions made at each layer, potentially leading to suboptimal expert utilization. To address this, researchers have introduced a novel approach called the Layerwise Recurrent Router for MoE (RMoE). This innovation leverages a Gated Recurrent Unit (GRU) to connect routing decisions across layers, allowing the model to learn from past routing choices. Unlike traditional routers that operate in isolation, RMoE considers historical information, effectively coordinating expert selection. This cross-layer information sharing improves the model's ability to find the right expert for the task, optimizing the use of available parameters. Extensive testing shows that RMoE models consistently outperform various baselines, regardless of model size or dataset. The added GRU doesn't significantly increase memory usage or training time. Notably, RMoE enhances existing methods without major modifications, making it readily compatible with other MoE advancements. Deeper analysis reveals that RMoE’s success comes from its ability to share information across layers, which promotes better exploration of possible expert combinations and encourages diversity among the experts themselves. This leads to more balanced routing decisions and more efficient utilization of the model’s experts. The development of RMoE marks a significant step towards more powerful and efficient LLMs, paving the way for future breakthroughs in AI technology by optimizing how these massive models learn and perform.