Top-p

Also known as: Nucleus Sampling

Top-$p$ (nucleus) sampling keeps the smallest set of tokens whose cumulative probability mass exceeds $p$ (e.g., 0.9), then samples from that set.

It adapts the candidate set to the distribution’s shape — tighter when the model is confident, wider when uncertain — and is often preferred over fixed top-$k$ alone.