arXiv:2605.24570v1 Announce Type: new Abstract: Despite the central role of optimization in deep learning, most optimizers rely on update structures whose functional form is fixed before training begins. This static design can limit their ability to respond to changing gradient behavior across the loss landscape, where training may shift between stable, noisy, and inconsistent regimes. This study proposes PILOT (Policy-Informed Learned OpTimizer), an online optimizer that adapts its update behavior during training. Rather than using a fixed balance between momentum, normalization, and sign-bas
The increasing complexity and resource demands of deep learning models necessitate more efficient and adaptive optimization methods to push capabilities further.
Improved optimizers can significantly accelerate AI research and development, potentially leading to faster training times, better model performance, and more efficient use of compute resources.
Deep learning training might become more adaptive and efficient, moving away from static optimization methods towards dynamic, policy-informed approaches that respond to training progress.
- · AI researchers
- · Deep learning developers
- · Cloud computing providers
- · Hardware manufacturers
- · Developers relying on fixed, sub-optimal training regimes
Reduced computational costs and time for developing large-scale AI models.
Faster iteration cycles in AI model development, leading to quicker advancements in various AI applications.
Potentially democratized access to advanced AI training for entities with fewer compute resources, if efficiency gains are substantial.
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Read at arXiv cs.LG