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Factors Influencing LLM Training Optimization
Even with meticulously designed configurations, the optimization process during Large Language Model training can sometimes diverge. The stability of LLM training is sensitive to and influenced by several key factors, including how parameters are initialized, the batching methods used, and the applied regularization techniques.
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Foundations of Large Language Models
Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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A research team successfully trains a 1-billion-parameter language model. Encouraged by their results, they scale up the exact same architecture and training setup to a 100-billion-parameter version using a much larger dataset. Midway through the training process, the model's loss value suddenly becomes
NaN(Not a Number), and the training crashes. This happens repeatedly despite restarting from previous checkpoints. Which of the following best explains this phenomenon?A machine learning team is training a very large language model and encounters several issues. Match each observed issue with the most likely underlying factor related to training stability.
Considerations for Stabilizing Large-Scale Model Training
Factors Influencing LLM Training Optimization