Hugo Vergnes trained a 3.8B-parameter language model scoring 0.384 on CORE using 65B tokens in 43 hours for $998, demonstrating that meaningful model training is accessible outside major labs. The project used a config-driven framework called little-lm with standard Llama-style architecture, and key improvements over earlier failed runs included better learning rate schedules, optimizer choices, and dataset selection.