SOURCE-LINKED INTELLIGENCE

LLM-as-an-Improver: Turning Verification into Better Candidates

arXiv · Artificial Intelligence · article · Sep 17, 2026 · UTC

Verifier-based selection improves LLM performance by generating multiple candidate solutions and using a verifier to select the most promising one. However, existing methods typically treat verification only as a ranking step and discard its feedback once a fixed candidate pool has been evaluated. In this paper, we ask whether verification can also improve the candidate set itself. To this end, we introduce LLM-as-an-Improver and propose Verify--Repair--Reselect (VRR), which uses verification feedback to generate and reselect improved candidates. VRR retains the initial winner while conditiona

Read original source ↗ Open in workspace

recordType

paper

region

Global