Revolutionizing Deep Learning: New Approach Tackles Training Instability!

This research introduces a fascinating fix for a common deep learning problem, the 'Infinite Gap.' By using a geometric alignment approach called Teacher-Free Self-Distillation, this method has the potential to dramatically improve the training process and enhance the performance of large language models. The innovation centers on preventing the optimizer from taking the 'lazy' route during training.

• •The method tackles the 'Infinite Gap' problem in deep learning, a source of instability.
• •It uses a novel 'Geometric Turn' employing negative squared Euclidean distance to bound logits.
• •Teacher-Free Self-Distillation (TFSD) allows models to act as their own teachers.