Pivot-SD: Efficient Self-Distillation for Masked Diffusion Language Models

Seo Hyun Kim, H, Younwoo Choi, Chen-Hao Chao, Se-Young Yun, Rahul G. Krishnan

Pivot-SD: Efficient Self-Distillation for Masked Diffusion Language Models: 17 upvotes on Hugging Face Daily Papers, #6 of 32 papers on 2026-10-05. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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