用强化学习动态优化检索生成中的查询分解，提升答案质量。
Query Decomposition for RAG: Balancing Exploration-Exploitation
- 将查询分解视为探索与利用的博弈，逐步判断是否继续当前子查询。
- 结合排序信息与人工评估，文档级精确率提升35%，α-nDCG提高15%。
- 适合需要长文本生成和高精度检索的系统开发者参考。
检索增强生成（RAG）系统通过将复杂请求分解为子查询，为每个子查询检索相关文档并聚合结果来生成答案。高效选择信息量大的文档需平衡两个关键目标：(i) 检索范围足够广以覆盖所有相关内容，(ii) 限制检索以避免噪声和计算开销。本文将查询分解与文档检索建模为一种探索-利用问题，每次检索一个文档后更新对子查询价值的信念，从而决定是继续利用当前子查询还是探索新方向。我们测试了多种贝叶斯强化学习方法，证明其在动态选择最有效子查询上的有效性。主要发现：利用排名信息与人工判断估计文档相关性，可使文档级精确率提升35%，α-nDCG提高15%，并在长文本生成任务中表现更优。
原文摘要 · Abstract (English)
Retrieval-augmented generation (RAG) systems address complex user requests by decomposing them into subqueries, retrieving potentially relevant documents for each, and then aggregating them to generate an answer. Efficiently selecting informative documents requires balancing a key trade-off: (i) retrieving broadly enough to capture all the relevant material, and (ii) limiting retrieval to avoid excessive noise and computational cost. We formulate query decomposition and document retrieval in an exploitation-exploration setting, where retrieving one document at a time builds a belief about the utility of a given sub-query and informs the decision to continue exploiting or exploring an alternative. We experiment with a variety of bandit learning methods and demonstrate their effectiveness in dynamically selecting the most informative sub-queries. Our main finding is that estimating document relevance using rank information and human judgments yields a 35% gain in document-level precision, 15% increase in α-nDCG, and better performance on the downstream task of long-form generation.
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