Writing an LLM from scratch, part 32i -- Interventions: what is in the noise?

The blog post discusses training a 163M-parameter GPT-2-style model from scratch using local and cloud resources, specifically focusing on the outcomes of various interventions during training and their effects on performance metrics. The author shares detailed insights into the impact of these changes, emphasizing the role of random weight initializations and their influence on training results. Key findings include the importance of certain training strategies and a greater stability found through consistent weight initialization, leading to better performance than random variations in training runs.