A machine learning team is training a new 10-billion-parameter language model on a novel, specialized dataset. They meticulously copy the exact training configuration (optimizer, learning rate schedule, parallelism strategy) from a famous research paper that successfully trained a model of a similar size. After several days, their training run becomes unstable and the model's performance collapses. What is the most probable explanation for this failure?

Evaluating an LLM Training Strategy

A research lab has a fixed computational budget to train a new large language model for a specific scientific domain. They have developed a promising initial configuration but are uncertain if it is optimal. Which of the following strategies represents the most effective and prudent use of their budget, given the complexities of establishing a stable and efficient training process?