TRIO: three specialized agents with aligned objectives refine retrieval-augmented generation
_reachsumit · x · 2026-11-04
TRIO is a multi-agent RAG framework that trains three agents under an iterative refinement paradigm with aligned objectives: a query-extension agent expands ambiguous queries with rewards based on improved answer quality; a judge agent, optimized via unanimity voting labels on evidence sufficiency, filters queries; and an answer agent is trained with dynamic matching-based rewards and curriculum learning to improve evidence use and accuracy. Extensive experiments across multiple benchmarks show effectiveness over state-of-the-art approaches, with code available.
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