title: DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for
    Dynamic Reranking in Retrieval-Augmented Generation
summary: Retrieval-augmented generation (RAG) systems combine large language models
  (LLMs) with external knowledge retrieval, making them highly effective for
  knowledge-intensive tasks. A crucial but often under-explored component of
  these systems is the reranker, which refines retrieved documents to enhance
  generation quality and explainability. The challenge of selecting the optimal
  number of documents (k) remains unsolved: too few may omit critical
  information, while too many introduce noise and inefficiencies. Although recent
  studies have explored LLM-based rerankers, they primarily leverage internal
  model knowledge and overlook the rich supervisory signals that LLMs can
  provide, such as using response quality as feedback for optimizing reranking
  decisions. In this paper, we propose DynamicRAG, a novel RAG framework where
  the reranker dynamically adjusts both the order and number of retrieved
  documents based on the query. We model the reranker as an agent optimized
  through reinforcement learning (RL), using rewards derived from LLM output
  quality. Across seven knowledge-intensive datasets, DynamicRAG demonstrates
  superior performance, achieving state-of-the-art results. The model, data and
  code are available at https://github.com/GasolSun36/DynamicRAG
paper_authors._id: 6823496167281062097f3ac6
paper_authors.name: Jiashuo Sun
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-14T07:35:31.777Z
paper_authors._id: 64527f701a57e1179c1c3693
paper_authors.avatarUrl: /avatars/25b2632d7aa9ce26d5d4924ecb00c4f4.svg
paper_authors.fullname: Jiashuo Sun
paper_authors.type: user
paper_authors.user: gasolsun
paper_authors._id: 6823496167281062097f3ac7
paper_authors.name: Xianrui Zhong
paper_authors._id: 6823496167281062097f3ac8
paper_authors.name: Sizhe Zhou
paper_authors._id: 6823496167281062097f3ac9
paper_authors.name: Jiawei Han
paper_title: DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for
    Dynamic Reranking in Retrieval-Augmented Generation
paper_summary: Retrieval-augmented generation (RAG) systems combine large language models
  (LLMs) with external knowledge retrieval, making them highly effective for
  knowledge-intensive tasks. A crucial but often under-explored component of
  these systems is the reranker, which refines retrieved documents to enhance
  generation quality and explainability. The challenge of selecting the optimal
  number of documents (k) remains unsolved: too few may omit critical
  information, while too many introduce noise and inefficiencies. Although recent
  studies have explored LLM-based rerankers, they primarily leverage internal
  model knowledge and overlook the rich supervisory signals that LLMs can
  provide, such as using response quality as feedback for optimizing reranking
  decisions. In this paper, we propose DynamicRAG, a novel RAG framework where
  the reranker dynamically adjusts both the order and number of retrieved
  documents based on the query. We model the reranker as an agent optimized
  through reinforcement learning (RL), using rewards derived from LLM output
  quality. Across seven knowledge-intensive datasets, DynamicRAG demonstrates
  superior performance, achieving state-of-the-art results. The model, data and
  code are available at https://github.com/GasolSun36/DynamicRAG
paper_ai_summary: DynamicRAG optimizes the retrieval-augmented generation framework by dynamically adjusting document selection and order based on a reinforcement learning agent trained with LLM output quality.
paper_ai_keywords: retrieval-augmented generation LLMs reranker reinforcement learning dynamic retrieval document selection LLM output quality