Turning Stale Gradients into Stable Gradients: Coherent Coordinate Descent with Implicit Landscape Smoothing for Lightweight Zeroth-Order Optimization
arXiv:2605.14373v2 Announce Type: replace Abstract: Zeroth-Order (ZO) optimization is pivotal for scenarios where backpropagation is unavailable, such as memory-constrained on-device learning and black-box optimization. However, existing methods face a stark trade-off: they are either sample-inefficient (e.g., standard finite differences) or suffer from high variance due to randomized estimation (e.g., random subspace methods). In this work, we propose Coherent Coordinate Descent (CoCD), a deterministic, sample-efficient, and budget-aware ZO optimizer. Theoretically, we formalize the notion of
The increasing complexity and resource demands of AI models are driving the need for more efficient optimization methods, particularly for edge and black-box applications.
This development offers a potential breakthrough for optimizing AI models in resource-constrained environments, expanding AI's applicability and reducing computational overhead.
Zeroth-order optimization methods could become more practical and widely adopted for on-device learning and black-box systems due to improved efficiency and stability.
- · Edge AI providers
- · Hardware manufacturers (for on-device AI)
- · AI researchers (optimizing complex models)
- · SaaS companies using black-box AI
- · Companies reliant solely on standard backpropagation for all AI deployments
More AI applications become feasible on lower-power devices.
Reduced dependency on large cloud-based compute for certain AI tasks, potentially decentralizing AI processing.
Enhanced data privacy as more AI processing can occur locally without sending data to the cloud for optimization.
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Read at arXiv cs.LG