基于广义瑞利商优化的基础模型保留适配
Foundation-Preserving Optimization in Generalized Eigenspace
AI总结：
提出FoLoRA框架，通过广义瑞利商优化更新方向，在微调中平衡下游任务性能与预训练能力保留。
摘要：
虽然微调有效地将基础模型适配到专门的下游任务，但可能会降低预训练期间获得的非目标能力。现有的遗忘感知方法通常通过专门的初始化或固定约束寻求更安全的更新，但未在训练过程中调节适配-保留权衡。我们提出基础保留LoRA（FoLoRA），一个遗忘感知优化框架。在一阶保留条件的指导下，FoLoRA定义了预训练代理激活上的遗忘惩罚和下游任务激活上的任务效用。然后，它通过广义瑞利商按单位遗忘惩罚的任务效用对更新方向进行评分。由此产生的谱坐标系实现了方向门控Adam更新，在训练过程中衰减低效用-惩罚方向。为了估计遗忘惩罚，FoLoRA通过从预训练模型中采样构建预训练代理校准数据，而不是依赖单个代理数据集。在数学、代码和指令遵循适配上的实验表明，FoLoRA在基线上实现了最强的保留-适配平衡，提高了目标任务性能，同时最好地聚合保留了非目标能力。
英文摘要：
While finetuning effectively adapts foundation models to specialized downstream tasks, it can degrade nontarget capabilities acquired during pretraining. Existing forgetting aware methods typically seek safer updates through specialized initialization or fixed constraints, but do not regulate the adaptation preservation trade-off during training. We propose Foundation Preserving LoRA (FoLoRA), a forgetting aware optimization framework. Guided by a first order preservation condition, FoLoRA defines a forgetting penalty over pretraining-proxy activations and a task utility over downstream task activations. It then scores update directions by task utility per unit forgetting penalty via a generalized Rayleigh quotient. The resulting spectral coordinate system enables direction wise gated Adam updates, attenuating low utility to penalty directions during training. To estimate the forgetting penalty, FoLoRA constructs pretraining proxy calibration data by sampling from the pretrained model rather than relying on a single proxy dataset. Experiments on math, code, and instruction following adaptation show that FoLoRA achieves the strongest preservation adaptation balance over baselines, improving target task performance with best aggregate preservation of non target capabilities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
