English(EN) AdaPaD: Adaptive Parallel Deflation for PEFT with Self-Correcting Rank Discovery

新的 AdaPaD 方法提高了大型语言模型 PEFT 的效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-11 15:44

研究人员推出了一种新颖的方法 AdaPaD，用于使用参数高效微调 (PEFT) 来高效微调大型语言模型。AdaPaD 同时训练所有秩-1 组件，每个组件针对一个在其他组件的估计值改进时会自我校正的分解目标进行优化。这种方法可以实现指数级衰减的误差，并允许动态秩发现，使秩分布成为输出而不是固定输入。 AI

影响 AdaPaD 提供了一种更高效的 LLM 微调方法，有望降低计算成本并实现更小的适配器尺寸。

排序理由该集群包含一篇学术论文，详细介绍了用于大型语言模型参数高效微调的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Anastasios Kyrillidis · 2026-05-11 15:44

AdaPaD: 用于PEFT的自校正秩发现自适应并行分解

Fine-tuning large language models with LoRA requires choosing a rank r before training starts. Existing approaches either extract rank-1 components sequentially, freezing each component's error permanently into every subsequent residual, or optimize the full low-rank factorizatio…

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AdaPaD: 用于PEFT的自校正秩发现自适应并行分解

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