Universal Assisted Generation: Faster Decoding with Any Assistant Model

How to generate text: using different decoding methods for language generation with Transformers

Assisted Generation: a new direction toward low-latency text generation

DC-Leap: Training-Free Acceleration of dLLMs via Draft-Guided Contiguous Leaping Decoding

arXiv:2607. 20467v1 Announce Type: new Abstract: While parallel decoding is central to the efficiency of Diffusion Large Language Models (dLLMs), current strategies are often hindered by overly conservative confidence thresholds.

Faster Text Generation with Self-Speculative Decoding

Gumbel Distillation for Parallel Text Generation

arXiv:2603. 22216v2 Announce Type: replace-cross Abstract: The slow, sequential nature of autoregressive (AR) language models has driven the adoption of parallel decoding methods.

WAND: Windowed Attention and Knowledge Distillation for Efficient Autoregressive Text-to-Speech Models

arXiv:2604. 08558v2 Announce Type: replace-cross Abstract: Recent decoder-only autoregressive text-to-speech (AR-TTS) models produce high-fidelity speech, but their memory and compute costs scale quadratically with sequence length due to full self-attention.