Summary of Scaling Law For Language Models Training Considering Batch Size, by Xian Shuai et al.
Scaling Law for Language Models Training Considering Batch Sizeby Xian Shuai, Yiding Wang, Yimeng Wu,…
Summary of Differential Learning Kinetics Govern the Transition From Memorization to Generalization During In-context Learning, by Alex Nguyen et al.
Differential learning kinetics govern the transition from memorization to generalization during in-context learningby Alex Nguyen,…
Summary of Simple and Provable Scaling Laws For the Test-time Compute Of Large Language Models, by Yanxi Chen et al.
Simple and Provable Scaling Laws for the Test-Time Compute of Large Language Modelsby Yanxi Chen,…
Summary of Low-bit Quantization Favors Undertrained Llms: Scaling Laws For Quantized Llms with 100t Training Tokens, by Xu Ouyang et al.
Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokensby Xu…
Summary of Towards Precise Scaling Laws For Video Diffusion Transformers, by Yuanyang Yin et al.
Towards Precise Scaling Laws for Video Diffusion Transformersby Yuanyang Yin, Yaqi Zhao, Mingwu Zheng, Ke…
Summary of Scaling Laws For Black Box Adversarial Attacks, by Chuan Liu et al.
Scaling Laws for Black box Adversarial Attacksby Chuan Liu, Huanran Chen, Yichi Zhang, Yinpeng Dong,…
Summary of Loss-to-loss Prediction: Scaling Laws For All Datasets, by David Brandfonbrener et al.
Loss-to-Loss Prediction: Scaling Laws for All Datasetsby David Brandfonbrener, Nikhil Anand, Nikhil Vyas, Eran Malach,…
Summary of Ultra-sparse Memory Network, by Zihao Huang et al.
Ultra-Sparse Memory Networkby Zihao Huang, Qiyang Min, Hongzhi Huang, Defa Zhu, Yutao Zeng, Ran Guo,…
Summary of Circuit Complexity Bounds For Rope-based Transformer Architecture, by Bo Chen et al.
Circuit Complexity Bounds for RoPE-based Transformer Architectureby Bo Chen, Xiaoyu Li, Yingyu Liang, Jiangxuan Long,…
Summary of Understanding Scaling Laws with Statistical and Approximation Theory For Transformer Neural Networks on Intrinsically Low-dimensional Data, by Alex Havrilla et al.
Understanding Scaling Laws with Statistical and Approximation Theory for Transformer Neural Networks on Intrinsically Low-dimensional…