AINeutralarXiv – CS AI · 10h ago6/10
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Navigating LLM Valley: From AdamW to Memory-Efficient and Matrix-Based Optimizers
A comprehensive arXiv survey examines the evolution of optimization algorithms for large language model training, moving beyond Adam toward memory-efficient, second-order, and matrix-based approaches. The research emphasizes that modern LLM optimization requires rigorous, scale-aware benchmarking that evaluates convergence, stability, memory usage, and implementation complexity rather than isolated speedup claims.