SplitThenMerge: Token-Level Skill-Compositional Sparse Mixture-of-Experts for Complex Domain-Specific Tasks

摘要

Large language models have demonstrated strong performance on general-purpose tasks but often fail to satisfy the accuracy requirements of knowledge-intensive domains such as law, medicine, and finance. Complex domain-specific generation is inherently compositional, involving multiple atomic skills such as reasoning, knowledge grounding, and numerical computation that are frequently interleaved at the token level. Existing domain adaptation methods typically train these heterogeneous skills jointly within a single objective, which makes it difficult for models to reliably coordinate multiple skills when solving complex tasks. In this work, we explicitly incorporate atomic skills into domain-specific model training and propose SplitThenMerge, a framework that decomposes domain competence into atomic skills, trains them independently, and composes them dynamically during generation. SplitThenMerge adopts a token-level sparse Mixture-of-Experts architecture to enable fine-grained skill routing and coordination while implementing each skill as a lightweight LoRA expert to achieve parameter-efficient specialization. Experimental results demonstrate that our method consistently achieves superior performance in both legal and medical domains under the same training parameter budget.