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Research questionHow can search agents learn when retrieval is necessary and ground answers in evidence without costly supervision?Sparse outcome rewards do not distinguish useful, evidence-grounded retrieval from redundant searching. Richer process supervision or LLM judging can provide that distinction but adds annotation or inference costs.
AI
AI Agents
Information Retrieval
Machine Learning
Natural Language Processing
Reinforcement Learning
Retrieval-Augmented Generation
Latest papersRecent research connected to this question, newest first.Search-G1: Grounded Search Agents via Representation-Based Intrinsic RewardsApplies to reinforcement-learning-based search agents for question answering. The supplied evidence covers multiple search-based QA benchmarks and two model scales, reporting competitive task accuracy with shorter response-side trajectories; it does not establish performance beyond those settings.research paper · Sep 4, 2026
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