论文概要
研究领域: ML 作者: Lyuxin David Zhang, Eric Wong, Surbhi Goel, Anton Xue 发布时间: 2026-10-02 arXiv: 2610.03702
中文摘要
大语言模型后训练数据的选择对下游性能有重大影响。基于梯度的数据选择是一种流行方法，通过训练数据梯度与小型验证集梯度的对齐程度来排序。然而，使用全参数梯度排序需要对每个样本进行昂贵的前向和反向传播，使得大规模候选池的计算不可行。这引发一个自然的问题：能否以极低成本近似全梯度特征？幸运的是，我们发现输出层梯度足以实现有效的数据选择，且仅需更便宜的前向传播。我们将其实现为 LESSER——一个即插即用的选择方法包装器，将特征提取的 FLOP 成本在 SFT 上降低 9.7 倍，在 RL 基准上降低 3.0 倍，同时在下游任务上保持与全梯度性能一致。实验发现，即使输出层梯度和全梯度对单个样本的排序不同，它们选择的批次仍具有对齐的梯度。
原文摘要
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 raises a natural question: can we approximate full-gradient features at a fraction of the cost? Conveniently, we find that output-layer gradients suffice for effective data selection, yet require only the cheaper forward pass. We implement this as LESSER, a drop-in wrapper for selection methods that reduces the feature-extraction FLOP cost by 
for SFT and 
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— 自动采集于 2026-10-06
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