This research paper investigates the 'curse of depth' phenomenon in large language models, exploring how increasing model depth can lead to optimization challenges and performance degradation. The study provides insights into the architectural trade-offs in transformer-based models and offers potential solutions to mitigate these depth-related issues.
Background
As language models grow larger and deeper, understanding the challenges of training very deep neural networks becomes increasingly important for AI research and development. The 'curse of depth' refers to the difficulties in optimizing and training extremely deep neural networks effectively.
- Source
- Lobsters
- Published
- Jun 14, 2026 at 04:12 AM
- Score
- 7.0 / 10