Complete guide to reinforcement learning for LLMs
cwolferesearch · x · 2026-08-24
A comprehensive guide to Reinforcement Learning (RL) for Large Language Models, covering everything from first principles to frontier research.
Key topics include:
- RL Fundamentals: General framework (policy, states, actions, transition functions, environment, rewards, trajectories) and core concepts (returns, discounting, probabilities, value/advantage functions).
- Basics of RL for LLMs: Token-level (MDP) vs. completion-level (bandit) formulations, outcome vs. process rewards, value estimation via critics, KL divergence, RLHF/RLVR setups, and importance sampling.
- Basic Policy Gradients: Deriving the RL objective and Vanilla Policy Gradient (VPG) from first principles.
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