Enabling artificial intelligence systems, particularly large language models, to integrate new 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 new information into a coherent framework usable across contexts. In this work, we argue that knowledge internalization 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）能够整合新知识并在推理过程中灵活运用，这仍然是一个核心挑战。现有的知识编辑方法侧重于原子事实，虽然改善了事实记忆，但往往未能将新信息整合成一个可在不同情境下使用的连贯框架。在本研究中，我们认为知识内化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进行训练：新信息对于解决任务至关重要，需与已有知识结合，并通过多步推理加以运用。基于这一见解，我们提出了一种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引入，为新颖事实提供语境，并解释其与现有知识的关系。其次，使用自生成的多跳问题对模型进行训练，这些问题需要涉及新信息的多步推理。第三，训练采用知识蒸馏方法，迫使学生模型内化教师的推理行为，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，并在需要结合多个新事实的挑战性问题上取得了显著性能。