MathyAIwithMike

Explore a groundbreaking paper on fine-tuning Large Language Models (LLMs) using Evolution Strategies (ES) at scale, bypassing traditional gradient-based methods. Discover how innovations like "virtual noise" and "in-place" perturbations overcome memory limitations, making LLM fine-tuning more accessible. Learn how this forward-pass-only system democratizes LLM optimization, enabling researchers and practitioners to fine-tune LLMs on less powerful hardware. Gain insights into the implications of this paradigm shift.