CAI-DLLM: Convergence Aware Inference for Diffusion Language Models
Diffusion language models can generate many tokens in parallel, but they still require repeated denoising steps during inference. This makes generation costly, especially when the model continues to recompute tokens that are already stable. To address these limitations, we propose CAI-DLLM, a training-free inference method that uses first-step confidence to guide denoising and reduce inference time. Specifically, CAI-DLLM commits easy tokens earlier, allocates more denoising steps to harder tokens, and adjusts decoding schedules across output blocks. As it relies only on first-step confidence signals, it does not require retraining, extra predictors, or weight updates. We evaluate CAI-DLL...
| Authors | Farhana Amin, Sabiha Afroz, Dimitrios S. Nikolopoulos |
|---|---|
| Primary category | cs.AI |
| Published | 23 August 2026 |
| Last updated at source | 23 August 2026 |
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