Paper explains LLM training bottleneck with universal 1/3 time scaling

Researchers have identified a universal one-third time scaling in the learning process of peaked probability distributions, a phenomenon observed in large language models. This behavior, stemming from the use of softmax and cross-entropy, creates a fundamental optimization bottleneck leading to slow power-law convergence of the loss and gradients. The findings offer a mechanistic explanation for observed neural scaling and suggest avenues for enhancing LLM training efficiency. AI

IMPACT Explains a fundamental bottleneck in LLM training, potentially guiding efforts to improve efficiency.

RANK_REASON This is a research paper detailing a theoretical finding about LLM training dynamics. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• LLMs
• Yizhou Liu

• paper
• other

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