Plan, Verify and Fill: A Structured Parallel Decoding Approach for Diffusion Language Models

• URL: http://arxiv.org/abs/2601.12247v1
• Date: Sun, 18 Jan 2026 03:53:01 GMT
• Title: Plan, Verify and Fill: A Structured Parallel Decoding Approach for Diffusion Language Models
• Abstract summary: Plan-Verify-Fill (PVF) is a training-free paradigm that grounds planning via quantitative validation.<n>PVF actively constructs a hierarchical skeleton by prioritizing high-leverage semantic anchors.<n>It reduces the Number of Evaluation Functions (NFE) by up to 65% compared to confidence-based parallel decoding.
• Score: 26.195809569100774
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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