Title: Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Information Retrieval, Large Language Model

Abstract: Recent advances have explored the use of large language models (LLMs) as retrievers by rewriting user queries, while other work has focused on expanding or augmenting corpus documents. However, such unidirectional augmentation, applied to queries or documents in isolation, often fails to reconcile the lexical and stylistic mismatches between them, limiting recall and overall retrieval robustness.
    To this end, we present an LLM-based retriever empowered to augment both user queries and corpus documents, with its policy fully explored via reinforcement learning (RL) and minimal human inductive bias. Notably, we find that simply training the LLM to augment queries and documents separately, even when combining both at inference, yields little benefit unless paired with our carefully designed bidirectional RL framework, which enables the LLM to simultaneously learn and collaborate on both query and document augmentation policies. A key technical challenge in realizing such a framework lies in jointly updating both policies during training, where the rewards for the two directions depend on each other, making their entangled reward intractable. Our approach addresses this by introducing a reward sampling strategy and a specifically designed RL algorithm that enables effective training with these sampled rewards. Experimental results demonstrate that our approach significantly enhances LLM-based retrieval performance in both sparse and dense settings, particularly in difficult retrieval domains, and achieves strong cross-benchmark generalization. Our code will be publicly released upon acceptance.