arXiv:2510.00419v2 Announce Type: replace Abstract: Zeroth-order optimizers have recently emerged as an attractive approach for fine-tuning large language models (LLMs), as they avoid backpropagation and can substantially reduce memory overhead relative to standard first-order training. However, existing zeroth-order methods rely on hand-crafted, static sampling strategies that are not adaptable to model-specific structures. To address this, we propose ZO-Finetuner, a learning-based zeroth-order optimizer for LLMs that automatically learns efficient perturbation strategies through a compact an
The increasing computational demands of large language models are pushing developers to find more efficient fine-tuning methods that bypass traditional backpropagation.
This development could significantly reduce the memory and computational overhead for training large AI models, accelerating their development and deployment across various applications.
Fine-tuning LLMs becomes more accessible and cost-effective, potentially decentralizing AI development and enabling more specialized applications without requiring massive compute resources.
- · AI developers with limited compute
- · On-device AI applications
- · Cloud providers via demand for more efficient but still substantial compute
- · Traditional high-memory GPU solutions
More efficient fine-tuning methods for LLMs reduce AI development costs and time.
This efficiency could lead to a proliferation of specialized LLMs for niche applications and greater competition in the AI market.
The reduced barrier to entry for LLM development could accelerate the rate at which AI agents become sophisticated and widespread.
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Read at arXiv cs.LG