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Synergy over Discrepancy: A Partition-Based Approach to Multi-Domain LLM Fine-Tuning

arXiv — cs.LG•Wednesday, November 12, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
The recent introduction of a partition-based multi-stage fine-tuning framework for large language models (LLMs) addresses the significant challenge of adapting these models across multiple heterogeneous domains, which is often hindered by inter-domain interference. This innovative approach strategically partitions domains into subsets, balancing discrepancies and synergies while adhering to model capacity constraints. The theoretical analysis backing this framework includes novel generalization bounds that validate the partitioning strategy. Empirical evaluations demonstrate that this method consistently surpasses state-of-the-art baselines in various language understanding tasks, indicating its effectiveness in enhancing LLM adaptability. As the demand for versatile AI applications grows, this framework could play a crucial role in advancing the capabilities of LLMs across diverse fields.
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