Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs

arXiv:2510.18245v3 Announce Type: replace-cross Abstract: Scaling the number of parameters and the size of training data has proven to be an effective strategy for improving large language model (LLM) performance. Yet, as these models grow increasingly powerful and widely deployed, the cost of...