Evolutionary Optimization of Model Merging Recipes
Authors: name: Takuya Akiba, name: Makoto Shing, name: Yujin Tang, name: Qi Sun, name: David Ha

We present a novel application of evolutionary algorithms to automate the
creation of powerful foundation models. While model merging has emerged as a
promising approach for LLM development due to its cost-effectiveness, it
currently relies on human intuition and domain knowledge, limiting its
potential. Here, we propose an evolutionary approach that overcomes this
limitation by automatically discovering effective combinations of diverse
open-source models, harnessing their collective intelligence without requiring
extensive additional training data or compute. Our approach operates in both
parameter space and data flow space, allowing for optimization beyond just the
weights of the individual models. This approach even facilitates cross-domain
merging, generating models like a Japanese LLM with Math reasoning
capabilities. Surprisingly, our Japanese Math LLM achieved state-of-the-art
performance on a variety of established Japanese LLM benchmarks, even
surpassing models with significantly more parameters, despite not being
explicitly trained for such tasks. Furthermore, a culturally-aware Japanese VLM
generated through our approach demonstrates its effectiveness in describing
Japanese culture-specific content, outperforming previous Japanese VLMs. This
work not only contributes new state-of-the-art models back to the open-source
community, but also introduces a new paradigm for automated model composition,
paving the way for exploring alternative, efficient approaches to foundation
model development.