An AI development team is fine-tuning a large language model to be a helpful assistant. They are considering two different strategies for creating the training dataset:

• Strategy A: Generate a very large dataset of 1 million examples, but focus on a single, high-frequency task: summarizing news articles.
• Strategy B: Generate a smaller but highly diverse dataset of 50,000 examples that cover hundreds of different tasks, such as creative writing, coding, question-answering, and planning.

Evaluate these two strategies in terms of their likely impact on the model's ability to correctly follow new, unseen instructions after training. Which strategy is superior for achieving this goal, and why? Justify your answer by explaining the trade-offs between dataset size, diversity, and the desired outcome.