Nemotron-TwoTower: Diffusion Language Modeling with Pretrained Autoregressive Context
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Computer Science > Computation and Language
Title:Nemotron-TwoTower: Diffusion Language Modeling with Pretrained Autoregressive Context
Abstract:Diffusion language models offer a promising alternative to autoregressive models due to their potential for parallel and iterative generation. However, existing approaches use a single network for both context representation and iterative denoising, forcing one model to serve both roles and limiting its capacity for either role. We propose TwoTower, a block-wise autoregressive diffusion model that decouples these roles into two towers: a frozen AR context tower that causally processes clean tokens, and a trainable diffusion denoiser tower with bidirectional block attention that refines noisy blocks via cross-attention to the context. Built on Nemotron-3-Nano-30B-A3B, an open-weight 30B hybrid Mamba-Transformer MoE model, and trained on approximately 2.1T tokens, Nemotron-TwoTower retains 98.7% of the autoregressive baseline's quality while offering 2.42X higher wall-clock generation throughput. We release the code and model weights at this https URL.
| Comments: | Code and model weights available at this https URL |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.26493 [cs.CL] |
| (or arXiv:2606.26493v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.26493
arXiv-issued DOI via DataCite (pending registration)
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