Harmony in Divergence: Towards Fast, Accurate, and Memory-efficient Zeroth-order LLM Fine-tuning

A recent study published on arXiv explores the use of zeroth-order optimization as a method for fine-tuning large language models (LLMs). This approach estimates gradients using only forward passes, which significantly reduces memory consumption compared to traditional methods (F2, F1). By lowering memory requirements, zeroth-order optimization enables fine-tuning of LLMs in environments with limited computational resources (F3). The study suggests that this technique could be transformative for deploying LLMs in real-world applications, where resource constraints often pose challenges (A1, A2). While these claims remain unverified, the method’s potential to balance efficiency and accuracy marks a notable development in AI research. This work aligns with ongoing efforts to optimize LLM performance and accessibility, as reflected in related recent studies. Overall, zeroth-order optimization presents a promising avenue for enhancing the practicality of large-scale language models.