Exploring Heterogeneous Model Merging Approach for Complex Knowledge Transfer
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Computer Science > Computation and Language
arXiv:2609.39369 (cs)
[Submitted on 30 Sep 2026]
Title:Exploring Heterogeneous Model Merging Approach for Complex Knowledge Transfer
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Abstract:Specialized models encode task-oriented behavior, but transferring that behavior to a general language model usually requires training, distillation, or representation alignment. We study whether such ability can instead be transferred directly at the parameter level. We apply two existing training-free heterogeneous merging methods, previously shown to transfer knowledge between general language models, to specialist-to-general transfer, projecting a specialist donor into the recipient's shape and interpolating backbone parameters without gradient updates or semantic alignment. Intersection-Merge (IM) injects a prefix-aligned donor slice matching the recipient shape, while Activate-Prune-Merge (APM) uses forward-pass activation statistics to select which donor dimensions to retain before injection. Across embedding, reranking, reward modeling, and MoE code-specialist transfer, both methods improve the general recipient, showing that simple heterogeneous merging can move capabilities across diverse specialist roles.
| Comments: | 6 pages, 1 figure, 7 tables. Preprint |
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.39369 [cs.CL] |
| (or arXiv:2609.39369v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.39369
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Exploring Heterogeneous Model Merging Approach for Complex Knowledge Transfer, by Jiahe Fan and 6 other authors
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