GlobalRAG: Enhancing Global Reasoning in Multi-hop Question Answering via Reinforcement Learning

GlobalRAG is a new reinforcement learning framework that significantly improves multi-hop question answering by decomposing questions into subgoals and coordinating retrieval with reasoning. The system achieves 14.2% average improvements in performance metrics while using only 42% of the training data required by baseline models.