LESSER: Post-Training Data Selection with Output-Layer Gradients

这条英文动态主要涉及模型能力与工程。原文要点：The choice of post-training data for large language models substantially affects downstream performance. Gradient-based data selection is a popular approach that ranks training data by how well their gradients align with those of a small validation set. However, ranking with full-parameter gradients requires an expensive backward pass on every sample, making computation intractable for large candidate pools. This rai...