This research compares two methods for creating powerful and aligned language models: merging and data mixing.

Merging, which combines pre-trained models, outperforms data mixing in terms of both performance and alignment. This suggests that merging is a promising approach for efficiently building more capable and aligned AI systems.

The findings are supported by other research exploring the benefits of combining diverse language models.