Evolutionary optimization of model merging recipes
Open accessAbstract
Large language models (LLMs) have become increasingly capable, but their development often requires substantial computational resources. Although model merging has emerged as a cost-effective promising approach for creating new models by combining existing ones, 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 optimization beyond just the weights of the individual models. This approach even facilitates cross-domain merging, generating models such as 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 substantially more parameters, despite not being explicitly trained for such tasks. Furthermore, a culturally aware Japanese vision–language model generated through our approach demonstrates its effectiveness in describing Japanese culture-specific content, outperforming previous Japanese vision–language models. 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.
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EvoLLM-JP uses evolutionary algorithms to optimally merge multiple foundation models into a single, specialized model with superior performance.
“EvoLLM-JP (Akiba et al., 2025) takes this further by using evolutionary algorithms to optimally merge multiple foundation models into a single, specialized model with superior performance.”
From A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence (2025) · cites this paper as Akiba et al., 2025
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- Authors
- Akiba, T., Shing, M., Tang, Y., Sun, Q., & Ha, D.
- Affiliations
- Sakana AI (Japan) (JP)
- Institute of Science Tokyo (JP)
- Corresponding author
- Ha, David
- Venue
- Nature Machine Intelligence
- Published
- 2025-01-27
- Volume
- 7
- Issue
- 2
- Pages
- 195-204
- ISSN
- 2522-5839 (ISSN-L)
- OpenAlex
- W4406844855
- Cited by
- 58
- References
- 18
- Citations by year
- 2026: 24 · 2025: 27 · 2024: 7
- FWCI
- 20.63
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Machine Intelligence (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Evolutionary Algorithms and Applications
- Keywords
- model merging, evolutionary optimization, large language models, open-source models, cultural awareness, parameter space optimization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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