Search-GRT improves search agents by training on ground-truth documents
_reachsumit · x · 2026-08-04
Samsung researchers propose Search-GRT, a guided retrieval training method for search agents that uses ground-truth-relevant documents during RL.
- The key idea is to restrict retrieval to documents that are actually relevant to the answer, giving the agent a denser learning signal.
- This reduces sparse-reward problems in multi-hop QA and helps the model learn better subquery generation and answer synthesis.
- The paper reports consistent gains over Search-R1 and says Search-GRT performs especially well on multi-hop question answering tasks.
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