LegalDrill: Diagnosis-Driven Synthesis for Legal Reasoning in Small Language Models

• URL: http://arxiv.org/abs/2604.23809v1
• Date: Sun, 26 Apr 2026 17:13:17 GMT
• Title: LegalDrill: Diagnosis-Driven Synthesis for Legal Reasoning in Small Language Models
• Abstract summary: Small language models (SLMs) are promising for real-world deployment due to their efficiency and low operational cost.<n>LegalDrill is a diagnosis-driven framework that extracts and iteratively refines reasoning trajectories from a capable teacher.<n>Experiments on several legal benchmarks demonstrate that LegalDrill significantly bolsters the legal reasoning capabilities of representative SLMs.
• Score: 20.07418966264218
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Small language models (SLMs) are promising for real-world deployment due to their efficiency and low operational cost. However, their limited capacity struggles with high-stakes legal reasoning tasks that require coherent statute interpretation and logically consistent deduction. Furthermore, training SLMs for such tasks demands high-quality, concise reasoning trajectories, which are prohibitively expensive to manually collect and difficult to curate via standard rejection sampling, lacking granularity beyond final verdicts. To address these challenges, we propose {LegalDrill}, a diagnosis-driven synthesis framework that extracts and iteratively refines reasoning trajectories from a capable teacher via fine-grained prompting, then a self-reflective verification is employed to adaptively select the most effective data for the SLM student. The resulting data empower SLM training through supervised fine-tuning and direct preference optimization. Extensive experiments on several legal benchmarks demonstrate that {LegalDrill} significantly bolsters the legal reasoning capabilities of representative SLMs while bypassing the need for scarce expert annotations, paving a scalable path toward practical legal reasoning systems.

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