TESS replaces per-sample weights with a Pointwise Value Matching objective, letting a data-selection network transfer across datasets and model scales in LLM safety and instruction tuning
The work reports that directly incorporating a selection network into existing meta-learning for training-data selection (MTS) objectives causes unstable optimization and poor generalization due to weight suppression and persistent reliance on easy-to-learn features, and proposes Transferable Example Scoring and Selection (TESS), a scalable data-selection framework built on a Pointwise Value Matching (PVM) objective, which in experiments on LLM safety and targeted instruction tuning demonstrates strong transfer across datasets, from subsets to full corpora, and from smaller to larger models.