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arXiv · 2606.00571

On the Difficulty of Learning a Meta-network for Training Data Selection

Zilin Du·Junqi Zhao·Boyang Albert Li
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Abstract

Synthetic data are increasingly used to train neural networks, yet distributional mismatch with real data limits their effectiveness when used indiscriminately. A common strategy is to learn data weights via bi-level optimization, which we refer to as Meta-learning for Training-data Selection (MTS). Interestingly, in practice, MTS often performs below expectation. We identify two obstacles in properly training MTS: a poor gradient signal-to-noise ratio (GSNR), which causes optimization difficulties, and lack of informative features that correlates with data quality. We present a mathematical analysis of MTS, which reveals the dynamics of normalized data weights and the relation between disparate data quality and poor GSNR. The analysis suggests a a simple yet effective solution: increasing the batch size. Further, we propose a set of informative features that capture the positions of training data in their distributions and training dynamics. Experiments across four benchmarks show consistent improvements, achieving average gains of 5.49% over training without selection and 2.89% over the strongest baseline.

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Zilin Du, Junqi Zhao, Boyang Albert Li. 2026-05-30. On the Difficulty of Learning a Meta-network for Training Data Selection. https://arxiv.org/abs/2606.00571

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