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cs.LG2026
SPARKLING: Balancing Signal Preservation and Symmetry Breaking for Width-Progressive Learning
Qifan Yu, Xinyu Ma, Zhijian Zhuo +7
Progressive Learning (PL) reduces pre-training computational overhead by gradually increasing model scale. While prior work has extensively explored depth expansion, width expansio…
cs.LG2026
LLMs as Noisy Channels: A Shannon Perspective on Model Capacity and Scaling Laws
Xu Ouyang, Deyi Liu, Yuhang Cai +5
Existing scaling laws for Large Language Models (LLMs), predominantly monotonic power laws, fail to explain emerging non-monotonic phenomena such as catastrophic overtraining and q…