Retrieval-augmented generation gets three separately optimized agents: TRIO splits query expansion, retrieval judging and answering
Retrieval-augmented generation (RAG), which fetches external material before a language model answers, often falters on weak retrieval and fabricated content. TRIO splits that pipeline into three agents, for query expansion, for judging whether retrieval is sufficient, and for answering, and lets each improve iteratively under its own reward signal: the expansion agent is rewarded by gains in answer quality, the judge agent is supervised by unanimity-voted sufficiency labels, and the answer agent trains with dynamic matching-based rewards plus curriculum learning. Experiments across multiple benchmark datasets report TRIO as more effective than state-of-the-art approaches, and the code is public.