Distribution Corrected Offline Data Distillation for Large Language Models

This research proposes an offline reasoning distillation framework for Large Language Models (LLMs) to enhance intelligence in resource-constrained environments. It tackles the distributional drift issue in existing offline methods by correcting teacher-student discrepancies while preserving data efficiency and supervision quality.