arXiv:2610.08108v1 Announce Type: new Abstract: Diffusion language models (dLLMs) have emerged as a promising alternative to autoregressive (AR) language models, offering flexible token-update orders...
arXiv:2606. 06712v1 Announce Type: cross Abstract: We study the transformation of autoregressive models (ARLMs) into diffusion language models (DLMs).
arXiv:2606. 11552v1 Announce Type: cross Abstract: Large language models (LLMs) achieve remarkable performance across a wide range of tasks, but their autoregressive decoding process incurs substantial inference costs due to inherently sequential token generation.
Forward-Free Diffusion Language Models with BPTT-Free Looped Refinement (FReDA) removes the need for a hand‑designed forward process in diffusion language modeling by treating model‑generated drafts as implicit intermediate states and refining them recursively. The approach detaches earlier refinement passes, backpropagating only through the final pass, and supports both self‑refinement and Best‑of‑N candidate selection. In sub‑8B experiments, FReDA‑4B surpasses larger diffusion baselines on reasoning and coding tasks, achieving up to 15% absolute gains and a 1.5‑1.8× speedup while scaling well with additional refinement steps.
arXiv:2603. 07475v4 Announce Type: replace-cross Abstract: Autoregressive (AR) language models build representations incrementally via left-to-right prediction, while diffusion language models (dLLMs) are trained through full-sequence denoising.
arXiv:2609.37974v1 Announce Type: cross Abstract: Masked diffusion models (MDMs) generate text by unmasking several tokens per step, but they are trained and sampled under different conditions. The m...
Large language models (LLMs) achieve remarkable performance across a wide range of tasks, but their autoregressive decoding process incurs substantial inference costs due to inherently sequential token generation. Speculative decoding addresses this bottleneck by employing a lightweight draft model to propose multiple future tokens that are subsequently verified in parallel by a larger target model.
PlaidQ is a 0.7B continuous diffusion language model designed for code generation. By distilling its iterative refinement trajectory into only a few denoising steps—or even a single step—PlaidQ achieves competitive performance with discrete diffusion models while dramatically reducing inference time. The study demonstrates that continuous diffusion can be effectively compressed, enabling efficient and accurate code generation with minimal computational overhead.
Zarya is a hybrid language model that jointly trains an autoregressive objective and a masked-diffusion objective within a single architecture. It structures training data into variable-size slots and uses a curriculum that gradually increases slot granularity, allowing a smooth transition from fine-grained AR learning to coarse-grained diffusion learning. At inference, Zarya offers two decoding modes—MDM sampling with first-hitting denoising and slotted speculative decoding that interleaves diffusion-based selection with autoregressive infilling—while fully decoupling training and inference regimes and supporting extensive configurability.
The study investigates how post‑training of large autoregressive language models (ARMs) into masked diffusion models (MDMs) affects their internal computation. Across two 7B ARM‑MDM families and four diagnostic tasks, the authors find that MDMs retain much of the ARM’s high‑attribution pathways on prefix‑dominant tasks, but reorganize computation toward earlier layers on globally constrained tasks. Component‑level probes reveal that ARMs depend on sharply specialized components, whereas MDMs show weaker specialization and more diffuse output‑space alignment.
arXiv:2607. 01170v1 Announce Type: cross Abstract: Generative reasoning re-rankers achieve strong recommendation accuracy by emitting a chain-of-thought before re-ordering a candidate list, but they are slow at inference: an autoregressive (AR) decoder spends one sequential forward pass per reasoning token, and the reasoning trace far exceeds the ranking it produces.
arXiv:2606. 19005v1 Announce Type: cross Abstract: Diffusion models have become a promising alternative to autoregressive models.