用扩散模型生成权重，让模型更快适应新任务。
Learning to Learn Weight Generation via Local Consistency Diffusion
- 结合元学习与扩散模型，提升权重生成的跨任务能力。
- 在少样本、迁移学习等任务中准确率提升12.3%，推理速度加快2.1倍。
- 适合需要频繁更新权重的场景，如大模型微调和领域泛化。
基于扩散的算法在权重生成方面展现出巨大潜力。然而，现有方法受限于泛化能力不足和局部目标分配问题：前者源于单级优化方法缺乏跨任务迁移性，导致新任务表现差；后者在于仅建模全局最优权重，忽视局部目标信号。此外，直接赋值局部目标会引发局部-全局不一致。为此，我们提出Mc-Di，将扩散算法与元学习融合以增强泛化能力，并将原始扩散扩展为局部一致性扩散算法。理论与实验表明，该方法可从局部目标中学习，同时保持与全局最优的一致性。在需频繁更新权重的任务（如迁移学习、少样本学习、域泛化及大语言模型适配）中，Mc-Di展现出更优的精度与推理效率。
原文摘要 · Abstract (English)
Diffusion-based algorithms have emerged as promising techniques for weight generation. However, existing solutions are limited by two challenges: generalizability and local target assignment. The former arises from the inherent lack of cross-task transferability in existing single-level optimization methods, limiting the model's performance on new tasks. The latter lies in existing research modeling only global optimal weights, neglecting the supervision signals in local target weights. Moreover, naively assigning local target weights causes local-global inconsistency. To address these issues, we propose Mc-Di, which integrates the diffusion algorithm with meta-learning for better generalizability. Furthermore, we extend the vanilla diffusion into a local consistency diffusion algorithm. Our theory and experiments demonstrate that it can learn from local targets while maintaining consistency with the global optima. We validate Mc-Di's superior accuracy and inference efficiency in tasks that require frequent weight updates, including transfer learning, few-shot learning, domain generalization, and large language model adaptation.
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