<p>Existing fine-tuning data selection methods for large language models (LLMs) often struggle to balance two aspects: the evaluation of legal fine-tuning data quality and the maintenance of syntactic diversity. The assessment of data quality consists of two dimensions: legal content quality, encompassing logical rigor, legal basis traceability, fact coverage, and actionable guidance, and instructional complexity, which measures how challenging an instruction–response pair is for the model to learn. Syntactic diversity, in contrast, captures the variety of sentence structures and dependency patterns within the dataset, reflecting how sentences differ in grammatical organization, for example through variations between active and passive voice, clause nesting, or modifier placement. To address these issues, this paper proposes LEAD (Legal Efficiency and Diversity), a fine-tuning data selection framework for legal LLMs that integrates dual-objective optimization with syntactic structure clustering. LEAD consists of two modules: the Legal Efficiency Training Score (LETS), which evaluates each sample’s legal quality, and Syntactic Diversity Clustering (SDC), which applies graph-kernel methods to ensure syntactic diversity. Experiments show that using only 5% of the original dataset selected by LEAD, the resulting model outperforms full-data training by 14.9% and achieves an average 12.3% improvement across legal benchmarks, demonstrating strong potential for efficient legal LLM fine-tuning.</p>