Plan, Verify and Fill: A Structured Parallel Decoding Approach for Diffusion Language Models
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866912857070764032 |
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| author | Li, Miao Jiang, Hanyang Cheng, Sikai Fu, Hengyu Cai, Yuhang Huang, Baihe Ye, Tinghan Chen, Xuanzhou Van Hentenryck, Pascal |
| author_facet | Li, Miao Jiang, Hanyang Cheng, Sikai Fu, Hengyu Cai, Yuhang Huang, Baihe Ye, Tinghan Chen, Xuanzhou Van Hentenryck, Pascal |
| contents | Diffusion Language Models (DLMs) present a promising non-sequential paradigm for text generation, distinct from standard autoregressive (AR) approaches. However, current decoding strategies often adopt a reactive stance, underutilizing the global bidirectional context to dictate global trajectories. To address this, we propose Plan-Verify-Fill (PVF), a training-free paradigm that grounds planning via quantitative validation. PVF actively constructs a hierarchical skeleton by prioritizing high-leverage semantic anchors and employs a verification protocol to operationalize pragmatic structural stopping where further deliberation yields diminishing returns. Extensive evaluations on LLaDA-8B-Instruct and Dream-7B-Instruct demonstrate that PVF reduces the Number of Function Evaluations (NFE) by up to 65% compared to confidence-based parallel decoding across benchmark datasets, unlocking superior efficiency without compromising accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_12247 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Plan, Verify and Fill: A Structured Parallel Decoding Approach for Diffusion Language Models Li, Miao Jiang, Hanyang Cheng, Sikai Fu, Hengyu Cai, Yuhang Huang, Baihe Ye, Tinghan Chen, Xuanzhou Van Hentenryck, Pascal Computation and Language Artificial Intelligence Machine Learning Diffusion Language Models (DLMs) present a promising non-sequential paradigm for text generation, distinct from standard autoregressive (AR) approaches. However, current decoding strategies often adopt a reactive stance, underutilizing the global bidirectional context to dictate global trajectories. To address this, we propose Plan-Verify-Fill (PVF), a training-free paradigm that grounds planning via quantitative validation. PVF actively constructs a hierarchical skeleton by prioritizing high-leverage semantic anchors and employs a verification protocol to operationalize pragmatic structural stopping where further deliberation yields diminishing returns. Extensive evaluations on LLaDA-8B-Instruct and Dream-7B-Instruct demonstrate that PVF reduces the Number of Function Evaluations (NFE) by up to 65% compared to confidence-based parallel decoding across benchmark datasets, unlocking superior efficiency without compromising accuracy. |
| title | Plan, Verify and Fill: A Structured Parallel Decoding Approach for Diffusion Language Models |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2601.12247 |