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During the pre-training of language models, performance can begin to decline after a certain point. This degradation is sometimes attributed to interference, where learning new information negatively impacts previously learned knowledge. To counteract this, a practical strategy is to implement early stopping, which involves halting the training process to prevent such interference and preserve the model's optimal performance.

Performance Degradation and Early Stopping in Pre-training

You are monitoring the pre-training of a large language model. The table below shows the validation loss measured at various training checkpoints. A lower validation loss indicates better performance. Based on this data, identify the optimal checkpoint to use for the final model and explain the reasoning behind your choice, referencing the phenomenon that this strategy is designed to prevent.

Analyzing a Language Model's Pre-training Log

A machine learning team observes that after a certain point in the pre-training process, their language model's performance on a validation dataset begins to consistently decline. Explain the potential underlying reason for this phenomenon and describe the strategic intervention used to mitigate it.

Rationale for Early Stopping in Model Pre-training

A machine learning engineer is pre-training a large language model. They monitor the model's performance on a separate, unseen dataset after every 10,000 training steps. They observe the following trend:
- Steps 1-100,000: Performance steadily improves.
- Step 110,000: The model achieves its best performance so far.
- Steps 120,000-150,000: Performance consistently worsens with each measurement.

Based on this observation, what is the most appropriate immediate action to ensure the best possible

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