#Llama-3-8B

The practical payoff is a theoretical licence to merge DPO-aligned checkpoints: sparser, disentangled updates mean less interference when combining model variants. Researchers working on preference alignment or model merging can use the support-pair bounds to reason about how many samples actually shape a fine-tune. Teams choosing between DPO and SFT for multi-task pipelines get a concrete 35× interference reduction figure to weigh.

Fine-tuning sculpts existing substrates rather than building pipelines from scratch, with suppression of below-L15 MLP contribution and two-layer advance of crystallization depth.