Fast-Decoding Diffusion Language Models via Progress-Aware Confidence Schedules

Diffusion large language models (dLLMs) offer a promising alternative to autoregressive models, but their practical utility is severely hampered by slow, iterative sampling.We present SchED, a training-free, model-agnostic early-exit algorithm that terminates diffusion decoding using a progress-aware confidence threshold.We evaluate SchED across multiple diffusion model families and a diverse set of benchmarks spanning multiple-choice, math, long-form QA, and translation.SchED delivers substantial acceleration: on instruction-tuned models, it achieves approximately 4× speedups while retaining baseline performance on average.On base models, SchED yields consistent speedup gains with 99.1-100% performance retention, with up to 2.34× under more aggressive settings.Under a conservative quality-penalized speed metric, SchED consistently outperforms prior confidence-based early-exit methods, including on long-form generation where existing approaches tend to break down.An entropy analysis of the model's token predictions reveals that instruction tuning speeds up the decay of predictive entropy.By leveraging inherent confidence stabilization as a signal for computational efficiency, SchED provides a robust framework for efficient dLLM inference.