arXiv Artificial Intelligence

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

2 hours ago 18.09.2026 07:00 👁 1 views
LLM-as-an-Improver: Turning Verification into Better Candidates

Quick summary

arXiv:2609.19515v1 Announce Type: new Abstract: 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.

Key takeaways

  • arXiv:2609.19515v1 Announce Type: new Abstract: 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.

Why it matters

The value of this work lies as much in how it was tested as in the claim itself. Sample design, baselines, uncertainty and replication help separate a laboratory result from real-world impact.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗