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GlobalRAG: Enhancing Global Reasoning in Multi-hop Question Answering via Reinforcement Learning
arXiv – CS AI|Jinchang Luo, Mingquan Cheng, Fan Wan, Ni Li, Xiaoling Xia, Shuangshuang Tian, Tingcheng Bian, Haiwei Wang, Haohuan Fu, Yan Tao|
🤖AI Summary
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.
Key Takeaways
- →GlobalRAG addresses two key limitations in multi-hop QA: lack of global planning and unfaithful execution of queries.
- →The framework introduces Planning Quality Reward and SubGoal Completion Reward to improve reasoning coherence.
- →GlobalRAG achieved 14.2% improvements in both EM and F1 scores using only 8k training samples.
- →The system uses 58% less training data than strong baseline models while delivering superior performance.
- →Progressive weight annealing strategy balances process-oriented and outcome-based learning objectives.
#reinforcement-learning#rag#question-answering#multi-hop#retrieval#reasoning#nlp#machine-learning#ai-research
Read Original →via arXiv – CS AI
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