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cs.LG2026
Support Tokens, Stability Margins, and a New Foundation for Robust LLMs
Deepak Agarwal, Dhyey Dharmendrakumar Mavani, Suyash Gupta +2
Self-attention is usually described as a flexible, content-adaptive way to mix a token with information from its past. We reinterpret causal self-attention transformers, the backbo…
cs.LG2024
Enhancing Stability for Large Language Models Training in Constrained Bandwidth Networks
Yun Dai, Tejas Dharamsi, Byron Hsu +2
Training extremely large language models (LLMs) with billions of parameters is a computationally intensive task that pushes the limits of current data parallel training systems. Wh…