DecoupleSearch: Decouple Planning and Search via Hierarchical Reward Modeling

DecoupleSearch is a groundbreaking approach that enhances Retrieval-Augmented Generation (RAG) systems by introducing hierarchical reward modeling. This innovation aims to improve the planning and search processes within large language models, making them more efficient and effective. By addressing the challenges faced by Agentic RAG, such as the need for high-quality planning and accurate search, DecoupleSearch represents a significant step forward in the field of artificial intelligence. This development is crucial as it not only boosts the performance of AI systems but also opens up new possibilities for their application in various domains.