AIBullisharXiv – CS AI · Mar 176/10
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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.