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The success of models like RoBERTa suggests a general principle for advancing pre-trained models: continuous performance improvements can be achieved by scaling up the training process, using more data and compute, even on relatively simple pre-training objectives.

General Direction for Pre-training: Scaling Simple Tasks

Critique the two approaches described in the case study below. Which one is more likely to result in a more capable and broadly effective model, and why?

Evaluating a Language Model Pre-training Strategy

A machine learning team is developing a new language model and is debating how to allocate their resources. They can either spend the next six months designing a novel, more sophisticated pre-training objective, or they can use their existing, simpler objective and focus all their efforts on acquiring a much larger dataset and securing more computational power for training. Based on the general principle demonstrated by the evolution of large-scale pre-trained models, which strategy is more likely to yield a more performant model? Justify your reasoning.

Prioritizing Pre-training Efforts

A research team is planning to pre-train a new language model with a fixed computational budget. One senior researcher argues, 'Instead of just using more data with our current simple masked language modeling objective, we should dedicate a significant portion of our budget to developing and implementing a novel, more complex pre-training task. This complexity is the key to unlocking better performance.' Based on the major findings from large-scale model training, which of the following statemen

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