Training-Free Dynamic Upcycling of Expert Language Models
Large Language Models (LLMs) have achieved remarkable performance on a wide range of specialized tasks, exhibiting strong problem-solving capabilities. However, training these models is prohibitively expensive, and they often lack domain-specific expertise because they rely on general knowledge datasets. Expertise finetuning can address this issue; however, it often leads to overspecialization, and developing a single multi-domain expert remains difficult due to diverging objectives. Furthermore, multitask training is challenging due to interference and catastrophic forgetting. Existing work proposes combining the expertise of dense models within a Mixture of Experts (MoE) architecture, a...
| Authors | Eros Fanì, Oğuzhan Ersoy |
|---|---|
| Primary category | cs.LG |
| Published | 31 March 2026 |
| Last updated at source | 31 March 2026 |
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