Efficient Training, Fast Inference

Описание: הרצאה זו היא חלק מכנס GenML 2025 של קהילת MDLI.
אתם יכולים לצפות בשאר ההרצאות ובמצגות פה: https://mdli.co.il/en25.


In this talk, I will discuss our ongoing efforts to make the training and adaptation of large foundation models more efficient and accessible. While foundation models (such as GPT5) achieve impressive performance, they require substantial memory and computational resources, limiting their broader application. I will present several recent methods that reduce training memory, accelerate convergence, and lower inference costs while preserving predictive accuracy. These include techniques that exploit the low-rank structure of gradient updates to enable memory-efficient fine-tuning, as well as methods that apply structured sparsity to identify compact and effective subnetworks within large models. Together, these advances enable the training, fine-tuning, and deployment of large models more quickly and efficiently, supporting a broader range of research and industry applications.