Enabling artificial intelligence systems, particularly large language models, to update knowledge and flexibly apply it during reasoning remains a central challenge. Existing knowledge editing approaches emphasize atomic facts, improving factual recall but often failing to integrate updated information into a coherent framework usable across contexts. In this work, we argue that knowledge update is fundamentally a reasoning problem rather than a memorization problem. Consequently, a model should be trained in situations where the new information is instrumental to solving a task, combined with pre-existing knowledge, and exercised through multi-step reasoning. Based on this insight, we propose a training strategy based on three principles. First, new knowledge is introduced as a coherent background story that contextualizes novel facts and explains their relation to existing knowledge. Second, models are trained using self-generated multi-hop questions that require multi-step reasoning involving the new information. Third, training is done using knowledge distillation, forcing a student model to internalize the teacher's reasoning behavior without access to the novel information. Experiments show that models trained with this strategy effectively leverage newly acquired knowledge during reasoning and achieve remarkable performance on challenging questions that require combining multiple new facts.

翻译：使人工智能系统（尤其是大型语言模型）能够更新知识并在推理过程中灵活运用，仍是一项核心挑战。现有的知识编辑方法侧重于原子事实，虽然提升了事实回忆能力，但往往无法将更新后的信息整合到可在不同语境中使用的连贯框架中。本文认为，知识更新本质上是一个推理问题而非记忆问题。因此，模型应在以下情境中接受训练：新信息有助于完成任务，需与既有知识结合，并通过多步推理进行实践。基于这一见解，我们提出基于三项原则的训练策略。首先，新知识以连贯的背景故事形式呈现，为新颖事实提供语境并阐明其与既有知识的关系。其次，模型利用自生成的多跳问题进行训练，这些问题需要涉及新信息的多步推理。第三，训练通过知识蒸馏方式进行，迫使学生模型内化教师模型的推理行为，而无需接触新信息。实验表明，采用此策略训练的模型在推理过程中能有效利用新获取的知识，并在需结合多个新事实的挑战性问题中展现出卓越性能。