arXiv:2607.06772v1 Announce Type: new Abstract: Learned optimization aims to improve upon hand-designed optimizers (e.g., Adam and Muon) by meta-learning small neural network optimizers over a distribution of tasks. While recent work has greatly advanced the architectural design and inductive biases of learned optimizers (LOs), current meta-training approaches still suffer from two main difficulties: (1) they cannot efficiently scale meta-training to long-horizon inner problems and (2) they often fail to surpass comparable hand-designed optimizers. To address these limitations, we propose Effi
This paper addresses critical limitations in learned optimization at a time when AI model complexity and training costs demand more efficient methods.
Improved meta-learning for optimizers could significantly accelerate the development and deployment of more sophisticated AI models, broadening their applicability and efficiency.
The ability to efficiently scale meta-training for long-horizon inner problems could make learned optimizers more competitive and eventually superior to hand-designed counterparts.
- · AI researchers and developers
- · Companies with large AI training needs
- · Deep learning frameworks
- · Traditional hand-designed optimizers (eventually)
More efficient and powerful AI models become feasible and widely accessible.
Reduced computational costs for AI training could lower barriers to entry for new AI applications and startups.
Accelerated AI development might intensify the compute supply chain demands or shift focus to more sophisticated hardware accelerators designed for these optimizers.
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Read at arXiv cs.LG