Training a 3.8B LLM to 0.384 CORE for $998

Independent developer Hugo Vergnes documented training a 3.8B-parameter language model to a 0.384 CORE benchmark score with a budget capped at $998. The experiment highlights how targeted dataset filtering, modern learning schedules, and compute frugality can squeeze competent small-scale models out of modest cloud GPU spend without corporate backing.