arXiv:2606.02857v1 Announce Type: new Abstract: Zeroth-order (ZO) optimization is a memory-efficient alternative to backpropagation for fine-tuning large language models, but its deployment is limited by the high variance of gradient estimation. We propose GRZO, a Group-Relative Zeroth-Order optimizer that draws one pseudo-independent perturbation per mini-batch example and aggregates the per-example losses through group-relative normalization, raising the effective gradient-direction count from one to the batch size at no additional forward cost while preserving inference-level memory. We pro
The continuous growth in Large Language Models (LLMs) requires more efficient fine-tuning methods, and this research addresses a significant limitation of existing zeroth-order optimization techniques.
Improved zeroth-order optimization for LLMs could democratize access to advanced AI fine-tuning by reducing memory and computational requirements, making sophisticated models more accessible.
Fine-tuning large language models could become significantly more memory-efficient and less computationally intensive, broadening the range of hardware capable of performing advanced model adjustments.
- · AI researchers with limited compute resources
- · Developers deploying LLMs on edge devices
- · Smaller AI firms
- · Hardware manufacturers of less specialized GPUs
- · Providers of highly specialized, expensive AI compute clusters
More widespread and cost-effective deployment and customization of Large Language Models.
An acceleration in the development and personalization of AI applications due to lower barriers to entry for fine-tuning.
Increased competition in the AI deployment space, potentially leading to more innovative and diverse AI products and services.
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Read at arXiv cs.LG