The Secrets to Training World-Class LLMs‎

Abstract: What does it actually take to train a strong language model? Published research makes it look clean: strategic architecture choices, carefully curated data, enough compute, and a tidy set of ablations where every decision seems obvious in hindsight. But those reports are written with a fair bit of rosy retrospection. They don’t capture the 2 a.m. debugging sessions, the loss spikes that appear from nowhere, or the subtle tensor-parallelism bug that quietly sabotages a run for days. The reality is messier and more iterative, and most of the decisions that actually mattered never make it into the final paper. In this lecture I’ll walk through the nuts and bolts of training an LLM end to end, from pretraining over trillions of tokens to post-training with reinforcement learning. 

Bio: Lewis is a Senior Research Scientist at Hugging Face, where he leads the post-training team to develop fully open models, datasets, and training recipes for the AI community. In a former life he was a theoretical physicist studying the low-energy sector of the Standard Model and developing precision tests for indirect dark matter searches.