Data-DPO: Direct Preference Optimization for Target Model Data Selection in LLM Post-Training

• URL: http://arxiv.org/abs/2608.16926v1
• Date: Wed, 05 Aug 2026 04:32:17 GMT
• Title: Data-DPO: Direct Preference Optimization for Target Model Data Selection in LLM Post-Training
• Abstract summary: We propose Data-DPO, a target model-oriented SFT data selection method.<n>Data-DPO observes the local training feedback of the target model on different samples through one-step probing.<n>It transforms activation differences among samples into pairwise data preferences, and trains a lightweight reward model to learn target-model-aware data preferences.<n>In the final selection stage, Data-DPO further combines target model preference, external quality scores, and marginal diversity to construct a more stable and effective training subset.
• Score: 36.73565681027731
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 attention to the compatibility between data and the capability distribution of the target model. To address this issue, we propose Data-DPO, a target model-oriented SFT data selection method. Data-DPO observes the local training feedback of the target model on different samples through one-step probing, transforms activation differences among samples into pairwise data preferences, and trains a lightweight reward model to learn target-model-aware data preferences. In the final selection stage, Data-DPO further combines target model preference, external quality scores, and marginal diversity to construct a more stable and effective training subset. Experimental results on Vision-Flan and LLaVA-CoT show that Data-DPO consistently outperforms existing data selection baselines under multiple data budgets and stably surpasses full data training performance.

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