[2512.13586] ReFusion: A Diffusion Large Language Model with Parallel Autoregressive Decoding

Computer Science > Computation and Language arXiv:2512.13586 (cs) [Submitted on 15 Dec 2025 (v1), last revised 5 Mar 2026 (this version, v2)] Title:ReFusion: A Diffusion Large Language Model with Parallel Autoregressive Decoding Authors:Jia-Nan Li, Jian Guan, Wei Wu, Chongxuan Li View a PDF of the paper titled ReFusion: A Diffusion Large Language Model with Parallel Autoregressive Decoding, by Jia-Nan Li and 3 other authors View PDF Abstract:Autoregressive models (ARMs) are hindered by slow sequential inference. While masked diffusion models (MDMs) offer a parallel alternative, they suffer from critical drawbacks: high computational overhead from precluding Key-Value (KV) caching, and incoherent generation arising from learning dependencies over an intractable space of token combinations. To address these limitations, we introduce \textsc{ReFusion}, a novel masked diffusion model that integrates sequence reorganization into the causal attention framework. By elevating parallel decoding from the token level to a higher slot level, \textsc{ReFusion} interleaves inter-slot diffusion-based selection with intra-slot autoregressive infilling, while reordering newly generated slots ahead of the remaining masks after each iteration. Consequently, this design simultaneously unlocks full KV cache reuse and reduces learning complexity from an intractable token combination space to a manageable slot-level permutation space. Extensive experiments on seven diverse benchmarks show that \...