A new approach, Branch-Train-MiX (BTX), streamlines the training of Large Language Models (LLMs) that are specialized in distinct domains. It merges the expertise of concurrently trained models into a singular Mixture-of-Experts (MoE) architecture.

• BTX starts by branching a seed model to train domain-specific experts.
• These experts are later united, combining their strengths in a cohesive MoE structure.
• This aggregated expertise leads to a significant performance advantage across multiple specialized tasks.

Such advancements in training efficiency could herald a new generation of AI, where deeply specialized skills are amalgamated in one powerful model, potentially enhancing both task-specific performance and generalizability.