arXiv:2601.17261v4 Announce Type: replace Abstract: Zeroth-Order (ZO) optimization has emerged as a promising solution for fine-tuning LLMs under strict memory constraints, as it avoids the prohibitive memory cost of storing activations for backpropagation. However, existing ZO methods typically employ isotropic perturbations, neglecting the rich structural information available during the forward pass. In this paper, we identify a crucial link between gradient formation and activation structure: the gradient of a linear layer is confined to the subspace spanned by its input activations. Lever
The increasing scale of LLMs highlights the urgent need for more memory-efficient fine-tuning methods, driving innovation in optimization techniques like Zeroth-Order (ZO).
This development could significantly reduce the computational and memory barriers to fine-tuning large language models, making advanced AI more accessible and efficient for broader applications.
LLM fine-tuning can now potentially proceed with significantly lower memory requirements by leveraging structural information in the forward pass, sidestepping the need for storing activations for backpropagation.
- · AI developers with memory constraints
- · Cloud providers offering LLM fine-tuning services
- · Developers of smaller, specialized LLMs
- · Traditional backpropagation-heavy fine-tuning methods
- · Companies without access to vast GPU memory
More efficient and cost-effective fine-tuning of large language models becomes possible through activation-guided zeroth-order optimization.
Broader adoption of custom and domain-specific LLMs as the barriers to fine-tuning decrease across various industries.
Increased competition among foundational model providers as specialized LLMs can be more easily developed and deployed, potentially decentralizing AI capabilities.
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Read at arXiv cs.LG