Merge++: Universal Merge Refinement Through Data-Free Checkpoint Inversion
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 them...
| Authors | Aditya Pola, Vineeth N. Balasubramanian |
|---|---|
| Primary category | cs.LG |
| Published | 19 September 2026 |
| Last updated at source | 19 September 2026 |
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