  Models trained on different datasets can be merged by a weighted-averaging of
their parameters, but why does it work and when can it fail? Here, we connect
the inaccuracy of weighted-averaging to mismatches in the gradients and propose
a new uncertainty-based scheme to improve the performance by reducing the
mismatch. The connection also reveals implicit assumptions in other schemes
such as averaging, task arithmetic, and Fisher-weighted averaging. Our new
method gives consistent improvements for large language models and vision
transformers, both in terms of performance and robustness to hyperparameters.
