pith:S4H6UNB3
Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities
Model merging combines trained models without new data or heavy retraining, and this survey organizes the methods into a fresh taxonomy while mapping their uses in language models and many other settings.
arxiv:2408.07666 v5 · 2024-08-14 · cs.LG · cs.AI · cs.CL · cs.CV
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This survey provides a comprehensive overview of model merging methods and theories, their applications in various domains and settings, and future research directions. Specifically, we first propose a new taxonomic approach that exhaustively discusses existing model merging methods.
The proposed taxonomy is exhaustive and the reviewed literature accurately represents the current state of model merging techniques without significant omissions or mischaracterizations.
The paper introduces a new taxonomy for model merging methods and reviews their applications in LLMs, MLLMs, continual learning, multi-task learning, and other subfields while outlining open challenges.
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| First computed | 2026-05-17T23:38:12.879582Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/S4H6UNB3RG2NF7OXXPN7QU2DJV \
| jq -c '.canonical_record' \
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Canonical record JSON
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