Yi-Fan Hao

Retrieval-Guided Fine-Tuning as Noisy Estimation: Risk bounds and Architectural Analysis

Under homoscedastic retrieval noise, it is shown that retrieval failure decays exponentially with task separation relative to noise, and explicit finite-sample conditions under which RAG-FT achieves lower risk than both target-only and full-corpus training are derived.