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Data-DPO: Direct Preference Optimization for Target Model Data Selection in LLM Post-Training
Not provided in the abstract
fine-tuningdata selectionmachine learningmodel optimization
2608.16926
Builder Relevance
2h ago80%
Abstract
Data-DPO is a method for selecting effective samples from large-scale data to reduce training costs while maintaining model performance.
Reality Card
Core Claim
Data-DPO outperforms existing data selection baselines and achieves better performance than full data training under multiple data budgets.
Method / Result
Data-DPO consistently surpasses full data training performance across various data budgets.
Limitations
The abstract does not specify limitations or reproducibility concerns.
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