何时接纳与接纳什么：面向数据为中心的小语言模型微调的梯度接纳方法

中文摘要 AI 辅助

LoRA微调在低秩更新子空间内使小语言模型（SLMs）适应异构指令数据，因此容易受到三个结构性问题的影响：相互抵消的冲突梯度、无法跟踪动态学习过程的静态数据选择，以及导致后续更新覆盖有用方向的子空间饱和。我们认为，有效的适应因此需要控制哪些由数据引发的梯度进入LoRA子空间以及何时进入。我们提出GRADE（梯度对齐的数据为中心配方），一个结合两种机制的数据为中心框架：一个状态感知的选择器，持续接纳与不断演变的多任务梯度场对齐的样本；以及一个自校准的步骤级门控，拒绝在接近饱和时可能导致破坏性覆盖的更新。在三个当前一代骨干网络和包含七个数据集的异构指令池上，GRADE在准确性和鲁棒性方面优于强数据选择和PEFT稳定化基线。它是唯一在每种架构上都能一致优于标准LoRA的方法，同时产生更连贯的梯度轨迹和更少的破坏性覆盖。这些结果表明，成功的小语言模型适应不仅取决于选择哪些数据，还取决于允许哪些梯度进入并持续存在于受限更新子空间中。

英文摘要

LoRA fine-tuning adapts small language models (SLMs) to heterogeneous instruction data within a low-rank update subspace, making it vulnerable to three structural problems: conflicting gradients that cancel, static data selection that cannot track evolving learning dynamics, and subspace saturation that causes later updates to overwrite useful directions. We argue that effective adaptation therefore requires controlling which data-induced gradients enter the LoRA subspace and when. We propose GRADE (GRadient-Aligned Data-centric rEcipe), a data-centric framework combining two mechanisms: a state-aware selector that continually admits samples aligned with the evolving multi-task gradient field, and a self-calibrating step-level gate that rejects updates likely to cause destructive overwrite near saturation. Across three current-generation backbones and a heterogeneous seven-dataset instruction pool, GRADE outperforms strong data-selection and PEFT-stabilization baselines in accuracy and robustness. It is the only method to improve consistently over standard LoRA on every architecture, while producing more coherent gradient trajectories and less destructive overwrite. These results show that successful SLM adaptation depends not only on which data are selected, but also on which gradients are allowed to enter and persist in the constrained update subspace.

发表机构

• Arizona State University（亚利桑那州立大学）
• Clemson University（克莱姆森大学）
• Meta AI
• NEC Laboratories America（NEC美国实验室）

机构由 AI 辅助整理，请以论文原文为准。