Merge++: Universal Merge Refinement Through Data-Free Checkpoint Inversion
Quick summary
arXiv:2609.22886v1 Announce Type: cross 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
Key takeaways
- arXiv:2609.22886v1 Announce Type: cross 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.
Why it matters
“Merge++: Universal Merge Refinement Through Data-Free Checkpoint Inversion” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
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