Selective contrastive training trims hallucinations with 10% data

Strong idea, because it turns hallucination reduction into a targeted margin problem instead of brute-force full-data alignment.

• –The self-generated negative sample is the right kind of hard negative for this problem: it is model-produced, task-matched, and cheap to obtain.
• –The selective gate is the main efficiency win; if the reported results hold, it avoids wasting updates on already-separated cases.
• –The approach is conceptually close to preference optimization, but with a more surgical loss window after first divergence.
• –Main caveat: the evidence here is still self-reported project-level validation, so reproducibility and benchmark breadth matter more than the headline gain.