Large language models (LLMs) are often used in environments where facts evolve, yet factual knowledge updates via fine-tuning on unstructured text often suffers from 1) reliance on compute-heavy paraphrase augmentation and 2) the reversal curse. Recent studies show diffusion large language models (dLLMs) require fewer training samples to achieve lower loss in pre-training and are more resistant to the reversal curse, suggesting dLLMs may learn new knowledge more easily than autoregressive LLMs (arLLMs). We test this hypothesis in controlled knowledge fine-tuning experiments and find that while arLLMs rely on paraphrase augmentation to generalize knowledge text into question-answering (QA) capability, dLLMs do not require paraphrases to achieve high QA accuracy. To further investigate whether the demasking objective alone can induce such a knowledge injection advantage in dLLMs regardless of their diffusion denoising paradigm, we propose masked fine-tuning for arLLMs, which prompts an arLLM to reconstruct the original text given a masked version in context. The masked fine-tuning for arLLMs substantially improves the efficacy of knowledge injection, i.e. no paraphrase needed and resistant to the reversal curse, closing the gap between arLLMs and dLLMs. We also demonstrate that the same demasking objective improves supervised fine-tuning (SFT) on math tasks over standard SFT, suggesting broader applicability of the demasking objective.

翻译：大语言模型（LLM）常应用于事实动态演化的环境，但通过非结构化文本微调进行事实知识更新通常面临两大挑战：1）依赖计算密集的释义增强；2）反转诅咒现象。近期研究表明，扩散大语言模型（dLLM）在预训练中能以更少的训练样本实现更低的损失，且对反转诅咒具有更强的抵抗能力，这暗示dLLM可能比自回归大语言模型（arLLM）更容易学习新知识。我们在受控知识微调实验中验证该假设，发现虽然arLLM需要依赖释义增强将知识文本泛化为问答（QA）能力，但dLLM无需释义即可实现高QA准确率。为深入探究仅凭去掩码目标是否能在dLLM中诱发此类知识注入优势（与其扩散去噪范式无关），我们提出针对arLLM的掩码微调方法，该方法通过上下文中的掩码版本提示arLLM重建原始文本。掩码微调显著提升了arLLM的知识注入效能（即无需释义且抵抗反转诅咒），缩小了arLLM与dLLM之间的差距。我们还证明相同的去掩码目标在数学任务的监督微调（SFT）中优于标准SFT，表明去掩码目标具有更广泛的适用性。