Diffutron: A Masked Diffusion Language Model for Turkish Language
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 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.
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Şuayp Talha Kocabay
claimed_verified
2026-03-24T09:21:27.159Z
668c299c95a7493f14fe3bcc
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/nMuuypcbWFQcDD5xNo4Zt.jpeg
Şuayp Talha Kocabay
suayptalha
user
suayptalha
69c2177bc105f45cb0481f43
Talha Rüzgar Akkuş
claimed_verified
2026-03-24T09:21:30.369Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1675246771355-noauth.jpeg
Talha Rüzgar Akkuş
Q-bert
user
Q-bert
Diffutron: A Masked Diffusion Language Model for Turkish Language
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 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.
Masked diffusion language models offer a resource-efficient, non-autoregressive approach to text generation for Turkish through progressive instruction tuning and LoRA-based pre-training.
masked diffusion language models LoRA-based continual pre-training multilingual encoder progressive instruction-tuning non-autoregressive text generation Turkish language modeling