Retrieval-augmented language models improve knowledge-intensive question answering, but they remain brittle on multi-step questions, where an early retrieval error can steer subsequent reasoning toward the wrong entity or relation. Existing methods can often detect that the current answer or evidence is unreliable, yet they rarely turn this diagnosis into an explicit retrieval correction. We propose Verification-Guided Retrieval Revision (VRR), a framework that uses verification as a control signal for multi-step retrieval. VRR couples a Generator that proposes search and answers, a Judge that verifies answer correctness and evidential support while emitting structured failure diagnoses, and a Retrieval Context Manager (RCM) that converts these diagnoses into retriever-facing updates such as clue selection, query rewriting, and state-aware evidence ranking. To make this loop learnable, we train VRR in three stages: iterative cold-start training to stabilize search, judge-shaped policy optimization to improve long-horizon reasoning, and diagnosis-conditioned RCM training for retrieval adaptation. Across seven open-domain QA benchmarks with a fixed retriever and corpus, training on HotpotQA and transferring to out-of-domain datasets, VRR surpasses the strongest prior baseline by 4.2 EM on average under budget-aligned evaluation, with larger gains on multi-hop benchmarks (+5.4).