What is a primary advantage of applying ongoing pre-training when fine-tuning a foundation model (FM)?

Ongoing pre-training, also known as continued pre-training, involves further pre-training a foundation model on a new, larger, or more domain-specific dataset before fine-tuning. This process allows the model to learn additional generalizable features and representations relevant to the target domain or task, thereby improving its overall understanding and adaptability. Consequently, when the model is then fine-tuned on a smaller, task-specific dataset, it starts from a more informed state, leading to better performance and generalization capabilities over time. Reducing model complexity is not a direct outcome; ongoing pre-training can even increase it by adding more learned parameters. While it can lead to faster convergence during fine-tuning due to a better starting point, it doesn't decrease the total time required to train the model, as it adds an extra pre-training step. Optimizing inference latency is typically addressed through model compression or hardware optimizations, not ongoing pre-training.