VRR lets LLMs verify, repair and generate candidates, improving code and reasoning results
A new arXiv paper proposes the LLM-as-an-Improver paradigm and introduces the Verify‑Repair‑Reselect (VRR) pipeline. VRR first generates a pool of candidate solutions, selects the top‑ranked winner with a verifier, then conditionally creates three additional alternatives: a repaired version of the winner, a repaired version of the runner‑up, and a solution derived from a fresh approach. All…
Key points
- VRR adds a verification feedback loop that creates repaired and novel alternatives after initial selection.
- Invalid or duplicate candidates are filtered using only inference‑time information.
- Benchmarks demonstrate VRR recovers correct answers even when the original pool lacks any.
The authors evaluate VRR across a range of large language models and on code‑generation as well as reasoning benchmarks. Results show consistent gains over traditional fixed‑pool verifier selection, and VRR can recover correct answers even when the initial pool contains no correct solution. The study suggests that verification feedback can play a broader role than ranking, actively constructing stronger candidates beyond the original set.
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