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[2602.18734] Rethinking Retrieval-Augmented Generation as a Cooperative Decision-Making Problem
Summary
This paper proposes a novel framework called Cooperative Retrieval-Augmented Generation (CoRAG), which reformulates retrieval-augmented generation as a cooperative decision-making problem, enhancing the interaction between generators and rerankers.
Why It Matters
The study addresses limitations in existing retrieval-augmented generation systems, which often rely on asymmetric dependencies. By promoting cooperation between components, CoRAG aims to improve the quality and stability of generated responses, making it relevant for advancements in AI and NLP applications.
Key Takeaways
- CoRAG redefines retrieval-augmented generation as a cooperative decision-making problem.
- The framework encourages collaboration between the generator and reranker for improved outcomes.
- Experimental results show enhanced generalization and stability with CoRAG.
- The model can perform effectively with limited training data (around 10K samples).
- This approach could influence future developments in AI and natural language processing.
Computer Science > Computation and Language arXiv:2602.18734 (cs) [Submitted on 21 Feb 2026] Title:Rethinking Retrieval-Augmented Generation as a Cooperative Decision-Making Problem Authors:Lichang Song, Ting Long, Yi Chang View a PDF of the paper titled Rethinking Retrieval-Augmented Generation as a Cooperative Decision-Making Problem, by Lichang Song and Ting Long and Yi Chang View PDF HTML (experimental) Abstract: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 reformulate RAG as a cooperative multi-agent decision-making problem and propose Cooperative Retrieval-Augmented Generation (CoRAG), a framework in which the reranker and the generator act 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...
