The generator proposes N attempts and the verifier chooses one, which can lift accuracy above the generator's single-attempt pass@1 rate. In the CLM-8B README, a fine-tuned Contrastive Language Model verified candidates from frontier models and raised DeepSWE from 73.7 percent to 81.6 percent on a small held-out set.