Cameron Wolfe publishes complete guide tracing RL for LLMs from VPG to GRPO variants
cwolferesearch · x · 2026-09-25
Cameron R. Wolfe released a comprehensive overview, "Reinforcement Learning for LLMs: The Complete Guide," building up from first principles to the research frontier.
- Role of RL in LLM history: RL enabled early instruction-following models, alignment and safety advances, and complex reasoning capabilities.
- Evolution of policy gradient algorithms: VPG → REINFORCE → PPO (Actor-Critic) → GRPO → GRPO variants, each explained intuitively.
- Frontier topics: reasoning, knowledge work, agents, token efficiency, and reliability are being tackled via RL.
- References Nathan Lambert's RLHF Book and links to deeper follow-up blogs for each section.
Related event: From VPG to GRPO: A Complete Guide to RL for LLMs(2 posts)→
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