GRASP: GRanularity-Aware Search Policy for Agentic RAG

Agentic retrieval-augmented generation (RAG) extends static RAG by allowing language models to iteratively reason, generate search queries, retrieve evidence, and predict answers. However, it remains challenging for models to decide when to retrieve, whether to use lexical matching or semantic similarity, and how to control context granularity to prevent irrelevant tokens from interfering with agent reasoning. In this paper, we introduce GRASP, a reinforcement learning (RL) framework for trainin

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ethics.ai (10 July 2026), “GRASP: GRanularity-Aware Search Policy for Agentic RAG,” evidence record 11341, https://ethics.ai/record/11341 (originally published by HuggingFace Daily Papers).

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