Should teams merge fine-tuned checkpoints instead of retraining or serving multiple models?
Model merging can capture the value of multiple fine-tunes without paying for full retraining or multi-model serving — reducing experimentation waste and inference duplication — but the ROI only works when the organization already has several compatible checkpoints and enough evaluation discipline to avoid shipping a bad merge.