Specialization without Sparsity: Efficient and Expressive Split-Path Experts for LLM Fine-Tuning

摘要

Parameter-efficient fine-tuning (PEFT) enables low-cost adaptation of large language models but often suffers from limited representational flexibility. To address this, we incorporate a Mixture-of-Experts (MoE) design and propose Efficient and Expressive split-path experts that enhance specialization while maintaining low resource overhead. Split-Path Adaptive Representation Mixture-of-Experts (SparMoE) replaces discrete hard routing with a soft routing and fully-activated mixture, enabling stable optimization. Each expert is parameterized as a split-path modulation module, consisting of a scaling path that promotes expert specialization and a bias path that preserves expert-specific signals. This design significantly enhances expressive capacity while maintaining strict parameter efficiency and architectural compatibility with PEFT. Extensive evaluations on GLUE, GSM8K, MBPP, and a text rewriting task from SmolTalk show that our approach consistently outperforms or matches state-of-the-art PEFT methods under comparable parameter budgets, achieving a favorable trade-off between adaptability and efficiency.