arXiv:2605. 00161v2 Announce Type: replace Abstract: Diffusion language models (DLMs) are an attractive alternative to autoregressive models because they promise sublinear-time, parallel generation, yet practical gains remain elusive as high-quality samples still demand hundreds of refinement steps.
By Hasan Amin, Yuan Gao, Yaser Souri, Subhojit Som, Ming Yin, Rajiv Khanna, Xia Song
The paper introduces a lightweight single‑layer sampler that allows masked diffusion language models to approximate joint sampling of multiple tokens in a single full‑model forward pass. By training the sampler to mimic exact joint sampling from a frozen diffusion model, the authors enable parallel unmasking of tokens while maintaining a close match to the true joint distribution. Experiments on Dream‑7B and Llada‑7B models show that unmasking four tokens per denoising step yields a MAUVE score of 0.87, a substantial improvement over the marginal baseline of 0.31.
By Parikshit Bansal, Sujay Sanghavi
The paper introduces LAW (Loopholing All the Way), a continuous generative model that relies entirely on Self‑Conditioning (SC) to propagate expected embeddings across diffusion steps, decoding tokens only once at the end. LAW outperforms both discrete and continuous diffusion models on Sudoku, Maze, and TinyGSM benchmarks, achieving higher accuracy and solving a larger proportion of GSM8K problems. Fine‑tuning a pre‑trained Uniform Diffusion Model with LAW significantly boosts its performance, and on OpenWebText LAW approaches autoregressive models while maintaining a favorable Pareto frontier.
By Adrien Deschenaux, Nadezhda Ilieva, Justin Deschenaux, Manuel Madeira, Dorina Thanou, Aur\'elien Lucchi, Antonio Orvieto, Caglar Gulcehre, Pascal Frossard
On-Policy Residual Learning for Diffusion Language Models (ORL) introduces a framework that learns residual context directly from model-generated trajectories, using an entropy-contraction objective to sharpen predictions. The approach co-designs algorithmic and system components—such as asynchronous residual capture, block-diffusion attention, and packed compute-balanced training—to enable practical scaling. Experiments on a 4B DLM converted from Qwen3-4B show up to 6.2× faster tokens per forward and 6.9× higher end-to-end throughput, while also improving speed-quality trade-offs over autoregressive models and transferring effectively to other DLMs like LLaDA-2.1-mini and DiffusionGemma.
By Yifan Yu, Yuqing Jian, Jisen Li, Yuezhou Hu, Tianyi Zhang, Xiaoxia Wu, Rui-Jie Zhu, Junxiong Wang, Zhongzhu Zhou, Fan Lai, James Zou, Chenfeng Xu
arXiv:2609. 04010v1 Announce Type: new Abstract: Large Language Models (LLMs) owe much of their success to next-token prediction (NTP), but their autoregressive (AR) structure requires slow, sequential token generation.
By Subham Sekhar Sahoo, Lingjie Chen, Khiem Pham, Jonathan Geuter, Chaitanya Dwivedi, Varad Pimpalkhute, Yash Akhauri, Alexander Moreno, Mikhail Yurochkin, Zhenting Wang, Mostafa Elhoushi, Nolan Dey, Shane Bergsma, Joel Hestness, John Thickstun, Eric Xing, Zhengzhong Liu
The paper introduces the Language Diffusion Transformer (LangDiT), a new transformer architecture tailored for continuous diffusion language models (CDLMs). By incorporating feature and query-key normalization, wider noise conditioning, likelihood-shortcut gradient routing, calibration, and confidence-gated training self-conditioning, LangDiT achieves lower generation perplexity than existing models across various CDLM formulations. Experiments on the OpenWebText dataset show state-of-the-art perplexity scores, demonstrating the architecture’s effectiveness while preserving the underlying model representations and training objectives.
By Weijian Luo, Yujian Chen, Hao Jiang, Guang Cheng, Tianyang Hu