GRASP: Retrieval Strategy for Agentic RAG
_reachsumit · x · 2026-07-14
GRASP is a new paper focusing on Agentic RAG, proposing a GRanularity-Aware Search Policy. It enables reinforcement learning agents to coordinate three retrieval granularities during multi-hop reasoning as needed: semantic search, keyword search, and passage reading.
The method aims to improve retrieval recall and QA accuracy for complex questions. The authors emphasize that this "granularity-aware" strategy doesn't lock into a single retrieval method; instead, the agent adaptively selects tools based on the current reasoning phase.
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