Large Language Diffusion Models
Authors: name: Shen Nie, name: Fengqi Zhu, name: Zebin You, name: Xiaolu Zhang, name: Jingyang Ou, name: Jun Hu, name: Jun Zhou, name: Yankai Lin, name: Ji-Rong Wen, name: Chongxuan Li

Autoregressive models (ARMs) are widely regarded as the cornerstone of large
language models (LLMs). We challenge this notion by introducing LLaDA, a
diffusion model trained from scratch under the pre-training and supervised
fine-tuning (SFT) paradigm. LLaDA models distributions through a forward data
masking process and a reverse process, parameterized by a vanilla Transformer
to predict masked tokens. By optimizing a likelihood bound, it provides a
principled generative approach for probabilistic inference. Across extensive
benchmarks, LLaDA demonstrates strong scalability, outperforming our
self-constructed ARM baselines. Remarkably, LLaDA 8B is competitive with strong
LLMs like LLaMA3 8B in in-context learning and, after SFT, exhibits impressive
instruction-following abilities in case studies such as multi-turn dialogue.
Moreover, LLaDA addresses the reversal curse, surpassing GPT-4o in a reversal
poem completion task. Our findings establish diffusion models as a viable and
promising alternative to ARMs, challenging the assumption that key LLM
capabilities discussed above are inherently tied to ARMs.