ReFusion: A Diffusion Large Language Model with Parallel Autoregressive Decoding
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 ReFusion, a novel masked diffusion model that achieves superior performance and efficiency by elevating parallel decoding from the token level to a higher slot level, where each slot is a fixed-length, contiguous sub-sequence. This is achieved through an iterative ``plan-and-infill'' decoding process: a diffusion-based planning step first identifies a set of weakly dependent slots, and an autoregressive infilling step then decodes these selected slots in parallel. The slot-based design simultaneously unlocks full KV cache reuse with a unified causal framework and reduces the learning complexity from the token combination space to a manageable slot-level permutation space. Extensive experiments on seven diverse benchmarks show that ReFusion not only overwhelmingly surpasses prior MDMs with 34% performance gains and an over 18times speedup on average, but also bridges the performance gap to strong ARMs while maintaining a 2.33times average speedup.
6940d86d65f1e24a117805cd
Jia-Nan Li
claimed_verified
2025-12-16T09:38:49.797Z
67be0888267276f5a2f9ce71
/avatars/c27dc0003351d2e59d7361064d572e55.svg
Jia-Nan Li
JinaLeejnl
user
JinaLeejnl
6940d86d65f1e24a117805ce
Jian Guan
claimed_verified
2025-12-17T10:09:09.824Z
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/avatars/a09c6abfa7c3d65d577168dfeee46dd9.svg
Jian Guan
Jiann
user
Jiann
6940d86d65f1e24a117805cf
Wei Wu
6940d86d65f1e24a117805d0
Chongxuan Li
ReFusion: A Diffusion Large Language Model with Parallel Autoregressive Decoding
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 ReFusion, a novel masked diffusion model that achieves superior performance and efficiency by elevating parallel decoding from the token level to a higher slot level, where each slot is a fixed-length, contiguous sub-sequence. This is achieved through an iterative ``plan-and-infill'' decoding process: a diffusion-based planning step first identifies a set of weakly dependent slots, and an autoregressive infilling step then decodes these selected slots in parallel. The slot-based design simultaneously unlocks full KV cache reuse with a unified causal framework and reduces the learning complexity from the token combination space to a manageable slot-level permutation space. Extensive experiments on seven diverse benchmarks show that ReFusion not only overwhelmingly surpasses prior MDMs with 34% performance gains and an over 18times speedup on average, but also bridges the performance gap to strong ARMs while maintaining a 2.33times average speedup.
ReFusion, a novel masked diffusion model, improves performance and efficiency by using slot-based parallel decoding, achieving superior results compared to autoregressive models and traditional masked diffusion models.
autoregressive models masked diffusion models Key-Value caching parallel decoding token level slot level diffusion-based planning autoregressive infilling slot-based design slot-level permutation space