PPO

PPO stands for Proximal Policy Optimization and is a method used to train an AI by giving it feedback — similar to how you teach a dog the right behavior using treats. The AI tries something out, receives an evaluation, and adjusts its behavior accordingly. This approach is called reinforcement learning: the AI doesn’t learn from ready-made examples, but from the consequences of its own actions. PPO itself is not a new goal, but a method that ensures the AI only changes its course by a small amount at each step. That might sound like a minor detail, but it makes the crucial difference between stable and chaotic training.

Why overly large learning steps destroy training

In reinforcement learning, the AI evaluates its own current strategy and tries to turn it into a better one. The problem: if it jumps too far in the process, the new strategy can suddenly turn out to be much worse than the old one. The model then overcorrects in the opposite direction — and never truly settles down. In practice, this means the training collapses.

Earlier methods tried to solve this problem mathematically, but they were computationally expensive and difficult to implement. PPO simplified this. It limits how much the strategy is allowed to change in a single step — achieving similar stability with significantly less effort. As a result, it has largely displaced older competing methods in practice.

The “prox” idea: staying close to the old strategy

The name reveals the principle: “Proximal” means “close by.” PPO allows the AI to improve its strategy, but only to the extent that the new version remains sufficiently similar to the old one. Technically, the method measures how much the probabilities of decisions differ before and after the update. If this deviation exceeds a certain threshold, a brake kicks in — the so-called clipping mechanism — automatically limiting the change.

The process runs in rounds: the AI gathers a batch of experiences, evaluates them, and then carries out several small updates — always with the same brake applied. Only then does a new round begin and new experiences are collected. This division makes training both stable and computationally efficient, because the same collected data is used multiple times before being discarded.

A common misconception is to confuse PPO with the actual goal of training. PPO is the tool, not the goal. What the AI is rewarded for — that is, what counts as a “good” answer — is a separate decision. In practice, a dedicated model is often used for this, one that mimics human evaluations and awards points to the main model during PPO training.

PPO in concrete products and current debates

OpenAI developed PPO itself and published it in 2017. The method first became known through games: an AI trained with it entirely on its own, without human guidance, and became better than any human player in complex real-time strategy games. Shortly afterward, PPO was also applied to language models — and today it is a core component of the method known as RLHF, used to align models like ChatGPT or Gemini with human feedback. RLHF stands for Reinforcement Learning from Human Feedback.

In news articles, PPO usually comes up in discussions about how to make a language model not just smart, but also helpful and safe. Pure language training teaches a model what text looks like — PPO teaches it what a human perceives as a good answer. This second phase is crucial to ensuring that a model doesn’t produce dangerous or nonsensical answers, but instead the ones users actually expect.

Recent research is testing whether PPO can be replaced by other methods that don’t require a separate evaluation model. So far, however, PPO remains the industry standard when it comes to refining large language models through feedback.