3 papers
cs.CL2026
Beyond a Single Extractor: Re-thinking HTML-to-Text Extraction for LLM Pretraining
Jeffrey Li, Josh Gardner, Doug Kang +10
One of the first pre-processing steps for constructing web-scale LLM pretraining datasets involves extracting text from HTML. Despite the immense diversity of web content, existing…
cs.CL2026
Relative Scaling Laws for LLMs
William Held, David Hall, Percy Liang +1
Scaling laws describe how language models improve with additional data, parameters, and compute. While widely used, they are typically measured on aggregate test sets. Aggregate ev…
cs.LG2025
Fantastic Pretraining Optimizers and Where to Find Them
Kaiyue Wen, David Hall, Tengyu Ma +1
AdamW has long been the dominant optimizer in language model pretraining, despite numerous claims that alternative optimizers offer 1.4 to 2x speedup. We posit that two methodologi…