paper
Understanding the Behaviors of Environment-aware Information Retrieval
paperactiveprovisional
understanding-the-behaviors-of-environment-aware-information-retrieval-b5472161·1 events·first seen 25h agoAliases: Understanding the Behaviors of Environment-aware Information Retrieval
Co-occurring entities
More like this (12)
Contextual RetrievalUncertainty-Aware Hybrid Retrieval for Long-Document RAGDifference-Aware Retrieval Policies for Imitation LearningAgentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Applicationsemantic retrievalWhen Does Mixing Help? Analyzing Query Embedding Interpolation in Multilingual Dense RetrievalPreference-Aware Rubric LearningSelf-Augmenting Retrieval for Diffusion Language ModelsOne Polluted Page Is Enough: Evaluating Web Content Pollution in Generative RecommendersObserve-and-Act Adaptive Context SelectionAttention Expansion: Enhancing Keyphrase Extraction from Long Documents with Attention-Augmented Contextualized Embeddingshybrid dense-sparse retrieval
Recent events (1)
RL-trained LLMs learn retriever-specific query formulation strategies for RAG
A new arXiv paper presents the first systematic study of using reinforcement learning to teach LLMs to adapt query formulation strategies to different retrieval backends. The authors find that different retrievers have surprisingly distinct optimal query styles (e.g., descriptive vs. question-like), making cross-retriever strategy transfer ineffective. They introduce a branching-based rollout technique to stabilize training over multi-step retrieval trajectories and show gains from retriever-specific human guidance and model scaling.