@DivyanshT91162: Autoregressive LLMs might already be getting replaced Someone built dLLM — an open-source library that can turn ANY aut…
Summary
dLLM is an open-source library that converts any autoregressive LLM into a diffusion LLM, enabling parallel decoding and faster text generation.
View Cached Full Text
Cached at: 05/17/26, 03:27 AM
Autoregressive LLMs might already be getting replaced
Someone built dLLM — an open-source library that can turn ANY autoregressive model into a diffusion LLM with minimal compute.
Yep… even LLaMA, Qwen, and GPT-style models.
Instead of generating text one token at a time, diffusion LLMs refine outputs in parallel like Stable Diffusion for text.
Meaning:
→ faster generation → parallel decoding → better editing & infilling → completely different scaling behavior
And the craziest part?
100% open-source.
Similar Articles
@DailyDoseOfDS_: Turn any Autoregressive LLM into a Diffusion LM. dLLM is a Python library that unifies the training & evaluation of dif…
dLLM is an open-source Python library that allows converting any autoregressive language model into a diffusion language model with minimal compute, unifying training and evaluation.
$R^2$-dLLM: Accelerating Diffusion Large Language Models via Spatio-Temporal Redundancy Reduction
R²-dLLM introduces spatio-temporal redundancy reduction techniques that cut diffusion LLM decoding steps by up to 75% while preserving generation quality, addressing a key deployment bottleneck.
@simplifyinAI: Researchers just made LLMs 8.5x faster with zero accuracy loss. It's called DFlash. It replaces the slow autoregressive…
Researchers introduced DFlash, a method that replaces autoregressive drafters with block diffusion models to achieve 8.5x faster LLM inference with zero accuracy loss.
@_avichawla: Researchers found a way to make LLMs 8.5x faster! (without compromising accuracy) Speculative decoding is quite an effe…
Researchers introduced DFlash, a technique using block diffusion models for speculative decoding that accelerates LLM inference by up to 8.5x without accuracy loss. It is already integrated with major frameworks like vLLM and SGLang.
Roll Out and Roll Back: Diffusion LLMs are Their Own Efficiency Teachers
This paper introduces WINO and WINO+, methods that enable revokable parallel decoding in diffusion LLMs and distill efficient denoising trajectories, significantly improving the quality-speed trade-off.