arXiv:2604.14669v2 Announce Type: replace Abstract: Zeroth-order (ZO) methods are widely used when gradients are unavailable or prohibitively expensive, including black-box learning and memory-efficient fine-tuning of large models, yet their optimization dynamics in deep learning remain underexplored. In this work, we provide an explicit step size condition that exactly captures the (mean-square) linear stability of a family of ZO methods based on the standard two-point estimator. Our characterization reveals a sharp contrast with first-order (FO) methods: whereas FO stability is governed sole
The continuous push for more efficient and scalable AI models, especially in resource-constrained environments, makes advancements in optimization techniques particularly relevant now.
This research provides a deeper understanding of zeroth-order optimization stability, critical for black-box learning and efficient fine-tuning of large AI models, potentially unlocking new applications and reducing computational costs.
Our understanding of the stability characteristics of zeroth-order methods is now more explicit, revealing a sharp contrast with first-order methods that could lead to more robust and performant learning algorithms.
- · AI researchers and developers
- · Companies utilizing black-box AI systems
- · Developers of memory-efficient AI applications
- · Inefficient AI optimization methods
- · Systems highly reliant on first-order methods for all tasks
Improved stability and efficiency in zeroth-order optimization methods for AI.
Broader adoption of zeroth-order methods in scenarios where gradients are inaccessible or costly, such as edge AI or privacy-preserving learning.
Acceleration of AI model development and deployment in diverse, resource-constrained environments, leveling the playing field for smaller developers or specific industrial applications.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG