What's this about?
Pivot-SD finds denoising commitments that sharply reduce uncertainty and trains on those pivotal token choices instead of every token or whole-step rewards.
What exactly is a masked diffusion language model?
It begins with many masked positions and repeatedly fills or revises them, potentially producing text in parallel rather than strictly left to right.
Why does this matter?
The method reports gains with only 200 questions and four rollouts each, showing that selective credit assignment may be more data-efficient than supervising an entire generated sequence.