AutoSelection discovers data recipes from a 90K instruction pool that outperform full-data training and other selectors on reasoning tasks for SFT across multiple models.
Lead: iterative data selection for efficient llm instruction tuning.Proceedings of the VLDB Endowment, 19(3):426–439
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From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning
AutoSelection discovers data recipes from a 90K instruction pool that outperform full-data training and other selectors on reasoning tasks for SFT across multiple models.