Transformer training dynamics studied via mechanistic analysis · 1 source tracked

Researchers have conducted a mechanistic study on the training dynamics of transformers, focusing on large-scale pretraining. Using the sparse modular addition task, they demonstrated that specialized attention circuits, termed clustering heads, can emerge during gradient descent to solve the problem. The study observed a two-stage learning process and identified loss spikes caused by the high curvature of normalization layers, offering insights applicable to large language model pretraining. AI

IMPACT Provides insights into the emergent learning mechanisms within transformers, potentially informing the pretraining of large language models.

RANK_REASON The cluster contains an academic paper detailing a mechanistic study of transformer training dynamics. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• Ambroise Odonnat
• clustering heads
• foundation models
• large language models
• sparse modular addition task
• Transformers

• paper
• infra

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