How does reinforcement learning inference-time steering work in ChatGPT systems?

Inference-time steering adjusts model outputs using reward signals or constraints without retraining the model.

Inference-time steering modifies generation behavior dynamically using reward models, classifiers, or constraint functions. Instead of retraining the entire model, external signals influence decoding by re-ranking tokens or adjusting probabilities.

Techniques include logit biasing, constrained decoding, and reward-guided sampling. This allows ChatGPT systems to adapt behavior for safety, style, or domain requirements in real time.

It is widely used to enforce safety policies and improve response alignment without expensive retraining.