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This design enables the update matrices to vary continuously along the diffusion process while keeping parameter and latency overhead negli","title":"NaRA: Noise-Aware LoRA for Parameter-Efficient Fine-Tuning of Diffusion LLMs","url":"https://arxiv.org/abs/2605.29716","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.29716v1 Announce Type: new \nAbstract: Diffusion Large Language Models (dLLMs) have emerged as a promising non-autoregressive generative paradigm. Given the prohibitive computational cost of full fine-tuning, Parameter-Efficient Fine-Tuning (PEFT) has become the standard approach. However, existing PEFT methods (e.g., LoRA), originally tailored for autoregressive models, rely on static parameters that are agnostic to the noise level. 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