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](https://huggingface.co/Dream-org/Dream-v0-Base-7B)
Current Diffusion Language Models (DLMs) have been studied at a smaller scale compared to their autoregressive (AR) counterparts and lack fair comparison on language modeling benchmarks. Additionally, training diffusion models from scratch at scale remains challenging. We propose adapting…
We introduce LLaDA 1.5, a competitive large diffusion language model, trained by variance-reduced preference optimization (VRPO).
Training Optimal Large Diffusion Language Models Jinjie Ni†, Qian Liu, Chao Du, Longxu Dou, Hang Yan, Zili Wang, Tianyu Pang, Michael Qizhe Shieh
The main features of jinjieni/quokka are: Language Diffusion Models, Training and Alignment.
Projects with overlapping indexed features include: jinjieni/megadlms — MegaDLMs. ml-gsai/llada-1.5 — We introduce LLaDA 1.5, a competitive large diffusion language model, trained by variance-reduced preference… hkunlp/diffullama — Current Diffusion Language Models (DLMs) have been studied at a smaller scale compared to their autoregressive (AR)… hkunlp/dream — ](https://huggingface.co/Dream-org/Dream-v0-Base-7B). autonomousvision/mdpo — [[Paper]](https://arxiv.org/pdf/2508.13148) [[Project]](https://cli212.github.io/MDPO/). amap-ml/ar-map — Are Autoregressive Large Language Models Implicit Teachers for Diffusion Large Language Models? A comprehensive…
