ZO-Act: Efficient Zeroth-Order Fine-Tuning via One-Shot Activation-Informed Low-Rank Subspaces
arXiv:2607.01125v1 Announce Type: new Abstract: Zeroth-order (ZO) optimization enables fine-tuning large language models when backpropagation is unavailable or memory-prohibitive, but existing methods often perturb full model weights or randomly constructed low-dimensional subspaces, yielding high-variance estimates and limited performance. We propose ZO-Act, an activation-informed ZO fine-tuning method that restricts perturbations to a fixed low-rank subspace derived from input activations. For each linear layer, ZO-Act computes a small activation basis once at initialization and optimizes on
The increasing scale and computational demands of large language models necessitate more efficient fine-tuning methods, especially when backpropagation becomes infeasible or memory-intensive.
This development offers a potential breakthrough for fine-tuning extremely large AI models, democratizing access to customization and enabling on-device or memory-constrained applications.
Fine-tuning large language models might become significantly less resource-intensive, broadening the scope of what is possible with existing hardware and potentially reducing the computational moat for advanced AI development.
- · AI hardware manufacturers (GPUs with less memory)
- · Small to medium AI development teams
- · On-device AI applications
- · Cloud AI service providers (optimizing resource usage)
- · Companies relying purely on large-scale compute advantage for fine-tuning
- · Inefficient fine-tuning methods
More efficient fine-tuning allows for broader application of LLMs in resource-constrained environments.
This could accelerate the deployment of highly customized and specialized AI agents, as fine-tuning becomes more accessible.
Reduced compute requirements for fine-tuning might ease the energy and financial burden of advanced AI development, potentially leading to more players in the advanced AI space.
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Read at arXiv cs.LG