arXiv Computation and Language August 28, 2026 By Ivan Kobyzev, Abbas Ghaddar, Yufei Cui

Survival-Guided Length Control for Efficient Diffusion Language Models

Read the original on arXiv Computation and Language →

The paper introduces a survival-guided length control method for diffusion language models (DLMs), framing length selection as a discrete-time survival problem over the end-of-sequence token. This training‑free, plug‑in length predictor can be added to any existing DLM and reduces unnecessary denoising steps. Experiments on reasoning and code‑generation benchmarks show up to a seven‑fold speedup in inference while maintaining task accuracy, and reveal that predicted lengths vary significantly even within the same dataset, affecting model performance.

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