FOAM: Frequency and Operator Error-Based Adaptive Damping Method for Reducing Staleness-Oriented Error for Shampoo
arXiv:2606.02365v1 Announce Type: new Abstract: Shampoo is attracting considerable attention for its superior performance on large-scale optimization benchmarks; yet it faces a significant practical bottleneck: the prohibitive computational overhead of matrix inversion. To mitigate this, practitioners typically rely on stale preconditioner updates, creating a fundamental trade-off between computational efficiency and optimization fidelity. In this work, we provide a theoretical study of staleness through the complementary lenses of convergence and stability. While staleness improves computatio
The increasing scale of AI models and optimization benchmarks intensifies the need for more efficient training methods, making computational overhead a critical bottleneck.
Improving the efficiency of large-scale optimization directly impacts the feasibility and cost of developing advanced AI, influencing who can compete in the AI frontier.
This research addresses a practical bottleneck in 'Shampoo' optimization, potentially making sophisticated AI training more computationally efficient and less prone to 'staleness-oriented error'.
- · AI model developers
- · Cloud computing providers
- · High-performance computing sector
- · Inefficient AI training methods
- · Companies with limited compute resources
Reduced computational costs and faster training times for large-scale AI models using 'Shampoo' optimization.
Accelerated development and deployment of more complex and higher-performing AI systems across various applications.
Lower barriers to entry for advanced AI development, potentially democratizing access to cutting-edge AI capabilities.
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Read at arXiv cs.LG