Since compute grows faster than the web, we think the future of pre-training lies in the algorithms that will best leverage ♾ compute We find simple recipes that improve the asymptote of compute scaling laws to be 5x data efficient, offering better perf w/ sufficient compute

🚨 NEW PAPER: "RLAD: Training LLMs to Discover Abstractions for Reasoning"! We introduce reasoning abstractions: concise insights that help LLMs solve hard reasoning problems by guiding structured exploration. 📄 arxiv.org/abs/2510.02263 🌐 cohenqu.github.io/rlad.github.io/ 🧵[1/N]