GRASP RL framework optimizes adaptive retrieval for agentic RAG
July 10, 2026
GRASP is a reinforcement learning framework that trains agents to coordinate semantic search, keyword search, and paragraph-reading actions. It enables models to adaptively control context granularity, retrieving sentence-level evidence only when necessary to prevent irrelevant token interference.
HOW THIS AFFECTS YOU
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builderYou can build RAG agents that reduce noise and latency by selectively retrieving fine-grained context.
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researcherThis provides a structured RL approach to optimizing multi-step retrieval reasoning.