Data-DPO: Direct Preference Optimization for Target Model Data Selection in LLM Post-Training
A new target-aware approach to data curation that optimizes fine-tuning efficiency and model performance.
Data selection in supervised fine-tuning aims to select a small set of effective samples from large-scale candidate data, reducing training cost while preserving model performance. However, existing methods usually treat data value as a relatively static property, and pay limited...
ArXiv CS.LG
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Data-DPO marks a shift from generic data curation to model-specific optimization by aligning training samples with the unique behavioral patterns of a target architecture. This method enables more efficient scaling, potentially allowing smaller, specialized models to outperform larger counterparts trained on broader, uncurated datasets.
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