Deterministic vs. Randomized Optimization: Efficiency Trade-offs for LLM Fine-Tuning

This paper explores the efficiency of deterministic versus randomized methods for optimizing smooth, nonconvex functions in high dimensions, particularly in the context of language model fine-tuning. The research demonstrates that deterministic methods are significantly less efficient, requiring substantially more function evaluations than randomized approaches when dealing with low-curvature traces. The findings suggest that the effectiveness of randomized methods in tasks like fine-tuning is tied to their ability to leverage these low-curvature conditions, which deterministic methods cannot match. AI

IMPACT This research highlights the computational trade-offs in optimizing large models, potentially influencing future algorithm design for LLM fine-tuning.

RANK_REASON The item is an academic paper published on arXiv detailing theoretical research on optimization methods. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• alphaXiv
• arXiv
• CatalyzeX
• cs.LG
• DagsHub
• Gotit.pub
• Hugging Face
• language model fine-tuning
• ScienceCast
• Yuxing Peng

• paper
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →