Expert Iteration

Expert iteration is a machine learning paradigm focusing on iteratively refining model performance through interaction with an expert source, such as human feedback or a superior model. Current research emphasizes efficient implementation, particularly within large language models (LLMs) and mixture-of-experts (MoE) architectures, addressing challenges like communication latency and computational cost through techniques like compiler optimization and sequential Monte Carlo planning. This approach shows promise in accelerating the development of high-performing AI agents across diverse domains, including mechanical design, mathematical reasoning, and game playing, by leveraging expert knowledge to guide the learning process and improve generalization.