AI RESEARCH
Reinforced Informativeness Optimization for Long-Form Retrieval-Augmented Generation
arXiv CS.CL
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ArXi:2505.20825v2 Announce Type: replace Long-form question answering (LFQA) requires open-ended long-form responses that synthesize coherent, factually grounded content from multi-source evidence. This makes reinforcement learning (RL) reward design critical. The reward must be verifiable for faithful grounding and stable optimization. However, many standard rewards assume a unique target with an exact-match notion of correctness, which fits short-form QA and math but breaks in