Causal Autoregressive Diffusion Language Model

• URL: http://arxiv.org/abs/2601.22031v1
• Date: Thu, 29 Jan 2026 17:38:29 GMT
• Title: Causal Autoregressive Diffusion Language Model
• Authors: Junhao Ruan, Bei Li, Yongjing Yin, Pengcheng Huang, Xin Chen, Jingang Wang, Xunliang Cai, Tong Xiao, JingBo Zhu,
• Abstract summary: CARD reformulates the diffusion process within a strictly causal attention mask, enabling dense, per-token supervision in a single forward pass.<n>Our results demonstrate that CARD achieves ARM-level data efficiency while unlocking the latency benefits of parallel generation.
• Score: 70.7353007255797
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we propose Causal Autoregressive Diffusion (CARD), a novel framework that unifies the training efficiency of ARMs with the high-throughput inference of diffusion models. CARD reformulates the diffusion process within a strictly causal attention mask, enabling dense, per-token supervision in a single forward pass. To address the optimization instability of causal diffusion, we introduce a soft-tailed masking schema to preserve local context and a context-aware reweighting mechanism derived from signal-to-noise principles. This design enables dynamic parallel decoding, where the model leverages KV-caching to adaptively generate variable-length token sequences based on confidence. Empirically, CARD outperforms existing discrete diffusion baselines while reducing training latency by 3 $\times$ compared to block diffusion methods. Our results demonstrate that CARD achieves ARM-level data efficiency while unlocking the latency benefits of parallel generation, establishing a robust paradigm for next-generation efficient LLMs.

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