Cameron Wolfe Publishes a Single-Document RL for LLMs Reference Covering Policy Gradients From REINFORCE to Current Frontier Training

Cameron R. Wolfe (Deep Learning Focus)·high signalXBlueskyLinkedInCopy link

Wolfe's August 24 post is built as a standalone reference rather than a news piece: it starts from RL first principles, walks the full evolution of the policy gradient algorithms actually used to train LLMs today, and then covers emerging research areas including reasoning, agents, token efficiency and reliability. Each section ends with a link to a deeper blog on that topic, and he credits the specific sources he synthesized, including Nathan Lambert's RLHF Book, Sutton and Barto, OpenAI's Spinning Up, Sebastian Raschka's from-scratch reasoning model book, Lilian Weng's policy gradient notes, and the TRL and OpenInstruct codebases. For a builder, this is the one link to keep for understanding why GRPO-family training behaves the way it does.