Rethinking Retrieval-Augmented Generation as a Cooperative Decision-Making Problem
On the map: Retrieval-augmented generation → Generation and context integration
Abstract (arXiv)
Retrieval-Augmented Generation (RAG) has demonstrated strong effectiveness in knowledge-intensive tasks by grounding language generation in external evidence. Despite its success, many existing RAG systems are built based on a ranking-centric, asymmetric dependency paradigm, where the generation quality of the generator is highly dependent on reranking results of the reranker. To overcome this limitation, we propose Cooperative Retrieval-Augmented Generation (CoRAG), a framework that treats the reranker and the generator as peer decision-makers rather than being connected through an asymmetric dependency pipeline. By jointly optimizing their behaviors toward a shared task objective, the reranker and generator are encouraged to cooperate, ensuring that document reranking and generation work in concert to improve the final response. Experimental results demonstrate good generalization and improved generation stability of CoRAG, even when the model is trained on only around 10K PopQA samples. Our model released in https://github.com/CoderrrSong/CoRAG.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Generation and context integration, one of 15 directions in Retrieval-augmented generation. Builds on 18 of its 30 references that are in the map. First posted on arXiv 21 Feb 2026.
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18 of 30 references.
Closest ancestors · 4
Same direction, newest first
- 2023Making Retrieval-Augmented Language Models Robust to Irrelevant Context2 Oct
- 2023In-Context Retrieval-Augmented Language Models31 Jan
- 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories20 Dec
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks22 May
Other references · 14
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2021Evaluating Large Language Models Trained on Codecited by 8,053
- 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,895
- 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
- 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,259
- 2023Enabling Large Language Models to Generate Text with Citationscited by 584
- 2022Decomposed Prompting: A Modular Approach for Solving Complex Taskscited by 554
- 2015Compositional Semantic Parsing on Semi-Structured Tablescited by 509
- 2024Retrieval-Augmented Generation for AI-Generated Content: A Surveycited by 352
- 2024InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationalescited by 100
- 2024HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systemscited by 37
- 2025A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directionscited by 30
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- 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
- 2024Evaluation of Retrieval-Augmented Generation: A Survey
- 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey
- 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generation
- 2025Enhancing Retrieval-Augmented Generation: A Study of Best Practices
- 2025Enhancing Retrieval-Augmented Generation with Two-Stage Retrieval: FlashRank Reranking and Query Expansion
- 2025DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented Generation
- 2025RAGSmith: A Framework for Finding the Optimal Composition of Retrieval-Augmented Generation Methods Across Datasets