Abstract
Masked Diffusion Language Models (MDLMs) have emerged as a compelling non-autoregressive alternative to standard large language models; however, their application to morphologically rich languages remains limited. In this paper, we introduce $\textit{Diffutron}$, a masked diffusion language model specifically designed for Turkish. Our approach leverages a resource-efficient training pipeline, starting with LoRA-based continual pre-training of a multilingual encoder on a large-scale corpus. To enable generative capabilities, we employ a progressive instruction-tuning strategy, sequentially adapting the model on general and task-specific instruction sets. Experimental results across comprehensive benchmarks demonstrate that, despite its compact size, our model achieves competitive performance compared to existing multi-billion-parameter baselines. These findings validate the effectiveness of masked diffusion modeling combined with multi-stage tuning for non-autoregressive text generation in Turkish.
Abstract (translated)
掩码扩散语言模型（MDLMs）已成为标准大语言模型的一个引人注目的非自回归替代方案；然而，其在形态丰富语言上的应用仍较为有限。本文中，我们介绍了专为土耳其语设计的掩码扩散语言模型$\textit{Diffutron}$。我们的方法采用资源高效的训练流程，首先基于LoRA对多语言编码器在大规模语料上进行持续预训练。为赋予模型生成能力，我们采用渐进式指令微调策略，依次在通用和任务特定指令集上对模型进行适配。综合基准测试的实验结果表明，尽管模型规模较小，我们的方法仍能达到与现有数十亿参数基线相比具有竞争力的性能。这些发现验证了掩码扩散建模结合多阶段微调在土耳其语非自回归文本生成中的有效性。
URL
https://arxiv.org/abs/2603.20466
