Zero-order Parameter-free Optimization for LMO-based Methods: Novel Approach for Efficient Fine-tuning

2026-06-12 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

AdaNAGED is a novel optimization method designed for efficient fine-tuning of large language models (LLMs), specifically addressing the significant memory overhead associated with backpropagation. This method unifies gradient-free training, adaptive tuning, and non-Euclidean update geometry. It leverages zeroth-order (ZO) optimization to reduce memory requirements, overcoming its typical sensitivity to stepsize and smoothing parameters by integrating parameter-free (PF) optimization. Additionally, AdaNAGED incorporates linear minimization oracle (LMO)-based methods to enable geometry-aware updates, which are crucial for handling the heterogeneous structure of LLM parameter blocks. The approach provides convergence guarantees and has been validated through large-scale LLM fine-tuning tasks using the OPT-1.3B model.

Key takeaway

For Machine Learning Engineers fine-tuning large language models on resource-constrained hardware, AdaNAGED offers a compelling solution. This method allows you to reduce memory overhead significantly by avoiding backpropagation, while also eliminating the need for costly manual hyperparameter tuning. Consider integrating AdaNAGED to achieve efficient, geometry-aware LLM fine-tuning, especially when working with models like OPT-1.3B, to streamline your development process and improve resource utilization.

Key insights

AdaNAGED unifies gradient-free, adaptive, and geometry-aware optimization for memory-efficient LLM fine-tuning.

Principles

• Zeroth-order optimization reduces memory overhead.
• Parameter-free methods adapt algorithmic parameters.
• LMO-based updates enable geometry-aware fine-tuning.

Method

AdaNAGED combines zeroth-order optimization with parameter-free adaptation and LMO-based non-Euclidean updates for LLM fine-tuning.

In practice

• Fine-tune LLMs like OPT-1.3B with less memory.
• Adapt optimization parameters without manual tuning.
• Apply geometry-aware updates to diverse parameter blocks.

Topics

• Large Language Models
• Fine-tuning
• Zeroth-order Optimization
• Parameter-free Optimization
• Linear Minimization Oracle
• OPT-1.3B

Best for: Research Scientist, AI Engineer, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.