Research
Survival-Guided Length Control for Efficient Diffusion Language Models
arXiv:2608.26374v1 Announce Type: new Abstract: Diffusion language models (DLMs) generate text by iteratively denoising masked sequences, but standard decoding either fixes the sequence length or reli
arXiv:2608.26374v1 Announce Type: new Abstract: Diffusion language models (DLMs) generate text by iteratively denoising masked sequences, but standard decoding either fixes the sequence length or relies on ad hoc stopping rules, often leading to unnecessary denoising steps. We recast length selection as a discrete-time survival problem over the end-of-sequence token and propose a plug-in, training-free length predictor that can be added to any existing DLM. Across reasoning and code-generation benchmarks, survival-guided length decoding speeds up inference by up to 7 times while preserving task accuracy. We further find that predicted lengths vary widely even within the same dataset, making model performance sensitive to the chosen length.
Related
- Stopping Computation for Converged Tokens in Masked Diffusion-LM Decoding
- Where and When to Commit: Candidate-Aware Decoding for Diffusion Language Models
- Just on Time: Token-Level Early Stopping for Diffusion Language Models
Source: arXiv cs.CL | 2026-08-28