A new research paper proposes a method to accelerate the training of large-scale transformers, called the Linear Growth Operator (LiGO). By utilizing the parameters of smaller, pre-trained models to initialize larger models, LiGO can save up to 50% of the computational cost of training from scratch while achieving better performance. This approach could have important implications for the field of AGI by enabling more efficient and effective training methods for large-scale models, and potentially leading to more flexible and adaptable models that can learn to grow and evolve over time. If this is already used to train GPT-5 it could mean that we get GPT-5 earlier than expected.