Merge++: Universal Merge Refinement Through Data-Free Checkpoint Inversion

Abstract:Model merging consolidates fine-tuned experts into one multi-task model without retraining. All existing data-free methods approach this problem entirely in weight space. Restricted to arithmetic on parameters, these methods never observe how each expert behaves, a signal that only emerges through forward evaluation. Accessing this behavioral signal requires inputs to evaluate on, which the data-free setting prohibits. We propose Merge++, a post-hoc method that addresses this by inverting the expert checkpoints to synthesize task-representative images, then distilling expert knowledge into the merged model using those images. Merge++ requires no additional data beyond the checkpoints themselves. It applies universally across merging algorithms and operates as a complementary refinement stage independent of the underlying weight-space method. The method consistently improves merging algorithms ranging from simple task arithmetic to state-of-the-art spectral methods, with average gains of +2 to +8 points and up to +25.9 on individual configurations.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2609.22886 [cs.LG]

(or arXiv:2609.22886v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.22886 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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