Only Ask What You Don’t Know: Grounded Delta Planning for Efficient Multi-step RAG
Wei-Chieh Chou · Xuanjun Chen · Jian-Ren Lin · Claire Lin · Hung-yi Lee · Jyh-Shing Roger Jang
GDP-RAG introduces a grounded delta planning mechanism for multi-hop RAG that halves reasoning steps while maintaining or enhancing performance through systematic compression, pruning, and refinement.
摘要
Multi-hop question answering remains challenging for Retrieval-Augmented Generation (RAG) because existing approaches either propagate errors across iterative retrieval rounds or over-generate reasoning steps, increasing cost without improving accuracy. We propose Grounded Delta Planning RAG (GDP-RAG), a plan-based framework that targets only the information delta based on three simple design choices: (1) preliminary retrieval to ground planning before execution, (2) a gap-conditioned planning prompt that asks only for missing information, and (3) a skeletal trajectory that pairs each subquery with a Thought capturing evidence from preliminary retrieval and carrying it through to the final answer. GDP-RAG focuses computation on unresolved gaps, yielding concise, reliable reasoning trajectories. Extensive experiments on HotpotQA, 2WikiMultiHopQA, and MuSiQue show that GDP-RAG achieves the highest accuracy (60.63\%) among all compared systems while maintaining a cost-of-pass of 0.51, 22\% lower than PAR-RAG (0.65) and 68\% lower than KnowTrace (1.57), with no method achieving both higher accuracy and lower cost.