arXiv:2606.16817v1 Announce Type: new Abstract: Recent retrieval-augmented generation (RAG) approaches have demonstrated strong capability in handling complex queries, yet current research overlooks a critical challenge: different retrievers require fundamentally different query formulation strategies for optimal performance. In this work, we present the first systematic analysis of how LLMs can learn to adapt their query formulation strategies for different retrievers via reinforcement learning (RL). Our empirical study reveals that RL effectively teaches an LLM to tailor its queries to speci
The accelerating development of advanced LLMs and their integration into retrieval-augmented generation systems is making the optimization of query formulation a critical bottleneck for performance.
This work directly addresses a core challenge in making RAG systems more robust and efficient, enabling LLMs to intelligently adapt to diverse data sources and retrieval mechanisms.
Current RAG approaches largely overlook retriever-specific query optimization; this research introduces a method for LLMs to dynamically learn and apply optimal querying strategies.
- · AI developers
- · Enterprise search solutions
- · Data scientists
- · Users of RAG-powered applications
- · Inefficient RAG systems
- · Manually-tuned retrieval pipelines
Retrieval-augmented generation (RAG) systems will become significantly more performant and adaptable, reducing the need for human-engineered query transformations.
This improved adaptability could accelerate the deployment of AI agents in complex data environments, as they can more effectively extract relevant information.
Enhanced RAG capabilities may lead to a broader adoption of AI in knowledge work, further displacing tasks reliant on manual information synthesis.
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Read at arXiv cs.CL