  Large language models show great potential in generating and optimizing code.
Widely used sampling methods such as Nucleus Sampling increase the diversity of
generation but often produce repeated samples for low temperatures and
incoherent samples for high temperatures. Furthermore, the temperature
coefficient has to be tuned for each task, limiting its usability. We present
Priority Sampling, a simple and deterministic sampling technique that produces
unique samples ordered by the model's confidence. Each new sample expands the
unexpanded token with the highest probability in the augmented search tree.
Additionally, Priority Sampling supports generation based on regular expression
that provides a controllable and structured exploration process. Priority
Sampling outperforms Nucleus Sampling for any number of samples, boosting the
performance of the original model from 2.87% to 5% improvement over -Oz.
Moreover, it outperforms the autotuner used for the generation of labels for
the training of the original model in just 30 samples.
