Fast and Fluent Diffusion Language Models via Convolutional Decoding and Rejective Fine-tuning
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arXiv
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| Format: | Preprint |
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2025
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| author | Seo, Yeongbin Lee, Dongha Kim, Jaehyung Yeo, Jinyoung |
| author_facet | Seo, Yeongbin Lee, Dongha Kim, Jaehyung Yeo, Jinyoung |
| contents | Autoregressive (AR) language models generate text one token at a time, which limits their inference speed. Diffusion-based language models offer a promising alternative, as they can decode multiple tokens in parallel. However, we identify a key bottleneck in current diffusion LMs: the long decoding-window problem, where tokens generated far from the input context often become irrelevant or repetitive. Previous solutions like semi-autoregressive address this issue by splitting windows into blocks (sacrificing bidirectionality), but we find that this also leads to time-interval expansion problem, sacrificing the speed. Therefore, semi-AR eliminates the main advantages of diffusion models. To overcome this, we propose Convolutional decoding (Conv), a normalization-based method that narrows the decoding window without hard segmentation, leading to better fluency and flexibility. Additionally, we introduce Rejecting Rule-based Fine-Tuning (R2FT), a post-hoc training scheme that better aligns tokens at positions far from context. Our methods achieve state-of-the-art results on open-ended generation benchmarks (e.g., AlpacaEval) among diffusion LM baselines, with significantly lower step size than previous works, demonstrating both speed and quality improvements. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_15188 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fast and Fluent Diffusion Language Models via Convolutional Decoding and Rejective Fine-tuning Seo, Yeongbin Lee, Dongha Kim, Jaehyung Yeo, Jinyoung Computation and Language Artificial Intelligence Machine Learning 68T50 I.2.7 Autoregressive (AR) language models generate text one token at a time, which limits their inference speed. Diffusion-based language models offer a promising alternative, as they can decode multiple tokens in parallel. However, we identify a key bottleneck in current diffusion LMs: the long decoding-window problem, where tokens generated far from the input context often become irrelevant or repetitive. Previous solutions like semi-autoregressive address this issue by splitting windows into blocks (sacrificing bidirectionality), but we find that this also leads to time-interval expansion problem, sacrificing the speed. Therefore, semi-AR eliminates the main advantages of diffusion models. To overcome this, we propose Convolutional decoding (Conv), a normalization-based method that narrows the decoding window without hard segmentation, leading to better fluency and flexibility. Additionally, we introduce Rejecting Rule-based Fine-Tuning (R2FT), a post-hoc training scheme that better aligns tokens at positions far from context. Our methods achieve state-of-the-art results on open-ended generation benchmarks (e.g., AlpacaEval) among diffusion LM baselines, with significantly lower step size than previous works, demonstrating both speed and quality improvements. |
| title | Fast and Fluent Diffusion Language Models via Convolutional Decoding and Rejective Fine-tuning |
| topic | Computation and Language Artificial Intelligence Machine Learning 68T50 I.2.7 |
| url | https://arxiv.org/abs/2509.15188 |