Sample-Adaptive Zeroth-Order Post-Training
Zeroth-order (ZO) optimization offers a memory-efficient alternative to backpropagation for fine-tuning large language models. However, existing ZO methods typically sample training examples uniformly, overlooking their evolving importance and leaving avoidable estimation variance.
The article introduces Sample-Adaptive Zeroth-Order Optimization (SAZO), a method to improve the efficiency of fine-tuning large language models by adaptively sampling training examples based on their importance.
What happened
The authors developed SAZO, a novel ZO optimization technique that adaptively allocates queries to training examples based on their estimated importance, reducing variance and accelerating convergence during model fine-tuning.
Why it matters
For AI developers and researchers, SAZO offers a memory-efficient alternative to backpropagation with demonstrable improvements in training efficiency, which is critical for optimizing large language models with limited computational resources.
- Adaptive sampling in ZO optimization reduces estimation variance and accelerates convergence.
- SAZO avoids true gradient computation, making it memory-efficient for large models.
- Exponential moving averages enable tracking of sample importance over time.
- The method demonstrates practical improvements across multiple model families.
The bigger picture
SAZO could reduce the computational cost of fine-tuning large models by up to 6× in query count while maintaining or improving performance, benefiting industries reliant on large-scale AI systems.
SAZO presents a significant advancement in zeroth-order optimization for fine-tuning large language models, offering improved efficiency and performance through adaptive sample importance estimation.
Technical details
- Sample-Adaptive Zeroth-Order Optimization (SAZO)
- Gradient-norm estimation from directional differences
- Per-example exponential moving averages
- O(N) additional scalar state for importance tracking
FAQ
What problem does SAZO solve?
SAZO addresses the issue of uniform sampling in zeroth-order optimization, which leads to avoidable variance and slower convergence during model fine-tuning.
How does SAZO estimate sample importance?
It uses gradient-norm information derived from directional differences without requiring access to true gradients, combined with exponential moving averages for per-example importance tracking.
What are the key benefits of SAZO?
SAZO achieves faster convergence (up to 6× reduction in query count), improved downstream performance, and requires minimal additional computational resources compared to existing methods.
This summary and analysis were generated with AI from the original article and may contain errors. Read the original for the full story.
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Originally published by arXiv cs.LG on Oct 12, 2026.
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