Merge Without Mayhem: How Orthogonal Deltas Could Revolutionize Model Composition
TL;DR for operators Model composition usually sounds harmless until someone asks the obvious production question: “Can we remove that client-specific update without retraining the whole thing?” At that point, many elegant AI stacks quietly become sedimentary rock. The MDM-OC paper proposes a cleaner model lifecycle: keep a shared base model, express every fine-tuned specialist as a task delta, orthogonalize those deltas so they interfere less, merge them with tuned coefficients, and subtract a selected delta later when a capability, customer, or data source needs to be removed.1 The important claim is not “we found another averaging recipe.” The claim is that model updates can be treated as separable components in parameter space. ...