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

On the Difficulty of Learning a Meta-network for Training Data Selection arXiv:2606.00571v1 Announce Type: cross 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 identi