A learned drafter predicts which masked position comes next

In block-diffusion decoding, a model chooses both what token to write and which masked position to fill. Exact decoding commits one position per pass, while faster multi-position methods can change the model's trajectory. Can a drafter propose several steps while preserving the target's own decoding choices?

SPOT learns which masked position a diffusion language model will fill next, then has the target verify a tree of candidate continuations in one pass.

SPOT predicts the next position from the target's most recent token probabilities, then builds candidate continuations for the target to check together. This lets it draft several possible steps while retaining a verification rule tied to the target's own choices.

In tests covering math and code tasks, the SDAR-8B target used fewer passes with SPOT than with Spiffy's calibrated draft graph, and SPOT had higher throughput. That measure counts generated tokens per generation-loop time relative to draft-free decoding on the same task, model and temperature.

The speed results come from specific models, tasks and decoding settings. Two transfer tests used an approximate prefix cache and therefore do not establish lossless decoding; verification costs can also erase speed gains, and at some settings on the smaller SDAR-1.7B model Spiffy was faster.