Understanding the Behaviors of Environment-aware Information Retrieval

Hugging Face Daily Papers Papers
reinforcement-learning retrieval-augmented-generation query-formulation llm information-retrieval environment-aware retriever-aware

Summary

This paper presents the first systematic analysis of how large language models can learn to adapt query formulation strategies for different retrievers using reinforcement learning, revealing distinct optimal query styles and introducing a branching-based rollout technique for multi-retrieval-step training stability.

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 specific retriever characteristics. We discover that different retrievers exhibit surprisingly distinct optimal query styles (e.g., descriptive vs. question-like), suggesting strategies learned for one retriever ineffective for another. We further show that performance can be enhanced by incorporating retriever-specific human guidance and by scaling model size. To facilitate learning over multi-retrieval-step trajectories, we introduce a branching-based rollout technique that improves training stability. Our work provides the first empirical evidence and actionable insights for building truly retriever-aware RAG systems. Code and resources are available at https://github.com/LCO-Embedding/Envs-aware-Information-Retrieval.
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Source: https://huggingface.co/papers/2606.16817

Abstract

Large language models can be trained via reinforcement learning to adapt query formulation strategies for different retrievers, with distinct optimal query styles and improved performance through retriever-specific guidance and model scaling.

Recentretrieval-augmented generation(RAG) approaches have demonstrated strong capability in handling complex queries, yet current research overlooks a critical challenge: different retrievers require fundamentally differentquery formulation strategiesfor optimal performance. In this work, we present the first systematic analysis of howLLMscan learn to adapt theirquery formulation strategiesfor different retrievers viareinforcement learning(RL). Our empirical study reveals that RL effectively teaches an LLM to tailor its queries to specific retriever characteristics. We discover that different retrievers exhibit surprisingly distinct optimal query styles (e.g., descriptive vs. question-like), suggesting strategies learned for one retriever ineffective for another. We further show that performance can be enhanced by incorporating retriever-specific human guidance and by scaling model size. To facilitate learning overmulti-retrieval-step trajectories, we introduce abranching-based rollout techniquethat improves training stability. Our work provides the first empirical evidence and actionable insights for building truly retriever-aware RAG systems. Code and resources are available at https://github.com/LCO-Embedding/Envs-aware-Information-Retrieval.

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