LLaDA2.0: Scaling Up Diffusion Language Models to 100B
Authors: name: Tiwei Bie, name: Maosong Cao, name: Kun Chen, name: Lun Du, name: Mingliang Gong, name: Zhuochen Gong, name: Yanmei Gu, name: Jiaqi Hu, name: Zenan Huang, name: Zhenzhong Lan, name: Chengxi Li, name: Chongxuan Li, name: Jianguo Li, name: Zehuan Li, name: Huabin Liu, name: Ling Liu, name: Guoshan Lu, name: Xiaocheng Lu, name: Yuxin Ma, name: Jianfeng Tan, name: Lanning Wei, name: Ji-Rong Wen, name: Yipeng Xing, name: Xiaolu Zhang, name: Junbo Zhao, name: Da Zheng, name: Jun Zhou, name: Junlin Zhou, name: Zhanchao Zhou, name: Liwang Zhu, name: Yihong Zhuang
Organization: Ant Group

This paper presents LLaDA2.0 -- a tuple of discrete diffusion large language models (dLLM) scaling up to 100B total parameters through systematic conversion from auto-regressive (AR) models -- establishing a new paradigm for frontier-scale deployment. Instead of costly training from scratch, LLaDA2.0 upholds knowledge inheritance, progressive adaption and efficiency-aware design principle, and seamless converts a pre-trained AR model into dLLM with a novel 3-phase block-level WSD based training scheme: progressive increasing block-size in block diffusion (warm-up), large-scale full-sequence diffusion (stable) and reverting back to compact-size block diffusion (decay). Along with post-training alignment with SFT and DPO, we obtain LLaDA2.0-mini (16B) and LLaDA2.0-flash (100B), two instruction-tuned Mixture-of-Experts (MoE) variants optimized for practical deployment. By preserving the advantages of parallel decoding, these models deliver superior performance and efficiency at the frontier scale. Both models were open-sourced.