Definition
Plain language
A reinforcement-learning method that nudges a model's behavior toward actions that scored higher.
As stated in the literature
A family of RL algorithms that estimate gradients of expected return with respect to policy parameters by sampling trajectories.
Also called: policy-gradient
Why it matters: It's the foundation under PPO, GRPO, and nearly every modern RL post-training method for LLMs.
For example, after a successful rollout, the policy is nudged to make the actions it took slightly more likely next time.
Heard on the show
“So you can reward or penalize that action directly with standard policy-gradient reinforcement learning.”Episode 114 — Agents That Rewrite Their Own Weights Instead of Just Taking Notes