Masked diffusion language models (dLMs) offer a promising parallel alternative to autoregressive models for complex reasoning.
However, they face a distinct credit-assignment challenge, since a few commitments during denoising sharply reduce the uncertainty over the remaining masked positions and shape much of the response.
Most post-training recipes for dLMs do not use this signal to decide which tokens to train on: they typically train on the final text or assign rewards to whole denoising steps, rather than selecting the individual commitments that shape the response.
We introduce Pivot-SD, an efficient offline self-distillation framework that supervises only these high-impact commitments (pivots).
Pivot-SD selects pivots using an information-gain metric measuring uncertainty reduction over the remaining masked positions.
Pivots from successful trajectories are trained with cross-entropy, and pivots from failed trajectories with targeted unlikelihood, leaving the rest of the failed trajectory untouched.
Using only 200 questions and four rollouts each, Pivot-SD improves LLaDA-8B-Instruct over full-sequence SFT and budget-matched diffusion RL baselines across math and code benchmarks.