Continual Pre-Training (CPT) is widely used for acquiring and updating factual knowledge in LLMs. This practice treats loss as a proxy for knowledge learning, while offering no grounding into how it changes during training. We study CPT as a knowledge learning process rather than a solely optimization problem. We construct a controlled, distribution-matched benchmark of factual documents and interleave diagnostic probes directly into the CPT loop, enabling epoch-level measurement of knowledge acquisition dynamics and changes in Out-Of-Domain (OOD) general skills (e.g., math). We further analyze how CPT reshapes knowledge circuits during training. Across three instruction-tuned LLMs and multiple CPT strategies, optimization and learning systematically diverge as loss decreases monotonically while factual learning is unstable and non-monotonic. Acquired facts are rarely consolidated, learning is strongly conditioned on prior exposure, and OOD performance degrades from early epochs. Circuit analysis reveals rapid reconfiguration of knowledge pathways across epochs, providing an explanation for narrow acquisition windows and systematic forgetting. These results show that loss optimization is misaligned with learning progress in CPT and motivate evaluation of stopping criteria based on task-level learning dynamics.

翻译：持续预训练（CPT）被广泛用于大型语言模型（LLM）中事实知识的获取与更新。该实践将损失函数视为知识学习的代理指标，但并未揭示其在训练过程中的实际变化机制。本研究将CPT视为知识学习过程而非单纯的优化问题，构建了一个分布匹配的受控事实文档基准，并将诊断探针直接嵌入CPT训练循环，实现了对知识获取动态及领域外（OOD）通用能力（如数学能力）变化的周期级测量。我们进一步分析了CPT如何重塑训练过程中的知识回路。通过对三个经过指令微调的LLM及多种CPT策略的实验发现：优化过程与学习过程存在系统性偏离——损失函数单调下降的同时，事实学习却呈现不稳定与非单调特性；获得的事实知识很少得到巩固，学习效果强烈依赖于先验接触经历，且OOD性能在训练早期即出现退化。回路分析揭示了知识通路在训练周期间的快速重构，这为狭窄的知识获取窗口和系统性遗忘现象提供了合理解释。这些结果表明，损失优化与CPT中的学习进程存在错位，从而启发了基于任务级学习动态的停止准则评估需求。