Paper Overview
Research Area: NLP Authors: Seo Hyun Kim, Sunwoo Hong, Younwoo Choi, Chen-Hao Chao, Se-Young Yun, Rahul G. Krishnan Published: 2026-10-02 arXiv: 2610.03665
Summary
Masked diffusion language models (dLMs) offer a promising parallel alternative to autoregressive models for complex reasoning. However, they face a distinct credit-assignment challenge: 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.
Method
Pivot-SD is an efficient offline self-distillation framework that supervises only these high-impact commitments, called pivots:
- Pivot selection: Pivots are chosen using an information-gain metric that measures the reduction in uncertainty over the remaining masked positions.
- Training signal: Pivots from successful trajectories are trained with cross-entropy loss; pivots from failed trajectories receive targeted unlikelihood training. All other tokens are left unchanged.
Results
Using only 200 questions and four samples per question, Pivot-SD enables LLaDA-8B-Instruct to surpass both full-sequence SFT and budget-matched diffusion RL baselines on mathematics and code benchmarks.
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*Automatically collected on 2026-10-06.*
Original arXiv link: https://arxiv.org/abs/2610.03665