Unlike autoregressive (AR) models, diffusion language models (DLMs) permit parallel token updates, flexible generation, and bidirectional attention, but leading systems rely on costly pretraining or f
BYOD converts autoregressive language models into diffusion models via parameter-efficient LoRA adapters, enabling parallel generation with minimal training cost while preserving base-model knowledge.
Unlike autoregressive (AR) models, diffusion language models (DLMs) permit parallel token updates, flexible generation, and bidirectional attention, but leading systems rely on costly pretraining or full-model fine-tuning. We present Build Your Own DLM (BYOD), a generalizable LoRA-only recipe for parameter-efficient AR-to-DLM conversion. Without architecture-specific tuning, BYOD induces diffusion generation in Gemma 2 9B, Llama 3.1 8B, Qwen2.5 7B, and Ministral 8B; to our knowledge, this is the first reported AR-to-DLM conversion of a Mistral model. The adapters comprise 4.1-5.8% of final parameters while every base-model parameter remains frozen; each 25,000-iteration conversion needs at…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
BYOD: Build Your Own DLM - Efficient Autoregressive-to-Diffusion Language Model Conversion was first seen on Hugging Face · Daily papers on Nov 3, 2026. Since then it has appeared in 10 checks on 2 different days. Its best position was #24 of 30, reached on Nov 3, 13:28 UTC. It dropped off the list after Nov 4, 03:28 UTC.
Position on Hugging Face · Daily papers at every check (lower is better).
Best rank per day (UTC).
Platform metric as reported by Hugging Face.
Archived daily, weekly and monthly lists that include this entry.
Not at the moment. It was last on the list on Nov 4, 03:28 UTC, after 2 days of trending.
#24 on Hugging Face · Daily papers, on Nov 3, 13:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 3, 2026 and has been on the list on 2 days (10 checks) since.