Token Routing

Token routing is a technique used to optimize the efficiency and performance of large language models (LLMs) and other deep learning architectures by dynamically controlling the flow of information between different processing units, such as experts in a Mixture-of-Experts (MoE) model or layers in a deep network. Current research focuses on developing adaptive routing mechanisms, including those that adjust the number of experts used per token or skip layers based on input characteristics, aiming to reduce computational costs while maintaining or improving accuracy. These advancements are significant because they enable the development of larger, more powerful models with reduced resource requirements, impacting both the scalability of LLMs and their deployment in resource-constrained environments.