Part 3/10:
As AI researchers dissect these emerging challenges, they are prompted to rethink how models can be trained and fine-tuned beyond mere increases in scale. Several noteworthy strategies have emerged:
- Hyperparameter Tuning: Focusing on optimizing how models learn from data during their pre-training phase could yield significant benefits. Researchers are experimenting with ways to remove duplicated data, with the belief that redundancy in training datasets can negatively impact performance.