Rethinking Pretraining: Data and Architecture

Large language model training follows a standard pipeline: tokenization, pretraining, possibly mid-training, and post training or alignment. Despite its wild success, we understand relatively little about this recipe and are almost certainly missing many opportunities to improve it. In this talk, I will focus on three such cases. I’ll describe our work on data efficient post training (e.g. LIMA, ALMA, and s1) where we argue that nearly all advanced model capabilities ultimately come from the pretraining data, even if effective alignment is still essential for controlling model behavior. I will also describe new methods for extracting more signal from the pretraining data, including new hierarchical architectures for byte-level language models (e.g. BLT) that are both tokenizer-free and scale better than traditional BPE-based methods, especially in the long tail. Finally, I will discuss decentralized, modular training algorithms (e.g. BTM) that better isolate and control the influence of specific data on specific model components and behaviors. Together, these methods promise to simplify training and improve scaling, by centering and amplifying the influence of data in architecture design.