AdaDR-RAG: Adaptive Agentic RAG Guided by Diversity and Reliability

Early Retrieval-Augmented Generation (RAG) approaches typically adopt a static paradigm where models passively incorporate externally retrieved knowledge to generate answers. In contrast, Agentic RAG reframes question answering as a dynamic interaction between the model and its environment characterized by an alternating cycle of reasoning and search engine invocation. However, existing studies in Agentic RAG suffer from two primary limitations. First, they retrieve a fixed number of knowledge snippets at each interaction step regardless of information sufficiency. Second, they predominantly focus on final answer accuracy while neglecting the diversity and reliability of the iterative interaction trajectories. To address these challenges, we propose AdaDR-RAG, an Adaptive Diversity and Reliability-guided Agentic RAG framework. First, an adaptive search controller dynamically modulates retrieval depth with an exploration-exploitation balancing mechanism, ensuring sufficient evidence accumulation while preventing degenerate query loops and context pollution. Second, a trajectory clustering strategy extracts cluster-level rewards to jointly optimize for diversity and reliability, preventing policy collapse via sparsity-aware incentives and ensuring factual consistency through intra-group consensus to stabilize multi-step reasoning. Experimental results demonstrate that our method outperforms state-of-the-art baselines on widely-used benchmarks for both general and multi-hop question answering.