这篇论文解决了语言模型持续学习中的核心痛点——遗忘问题，做模型微调或持续训练的团队可以直接用自生成回放方法，省去存储旧样本的麻烦，值得关注。
该论文研究了语言模型在新任务训练时遗忘旧知识的问题，并提出利用模型自身生成的样本作为回放数据，几乎可以消除遗忘。研究发现，当模型容量接近饱和时，遗忘仍会发生，因为模型无法吸收新信息而不覆盖旧知识。在容量充足的情况下，低学习率可以减少遗忘但需要更多训练步骤，而自生成回放打破了这一权衡，允许高学习率微调而不遗忘。这一方法避免了传统回放需要存储旧样本的不可行性，为持续学习提供了实用方案。
Forgetting in Language Models: Capacity, Optimization, and Self-Generated Replay
Models trained on a new task typically degrade on prior tasks, a phenomenon known as forgetting. Traditionally, mitigating forgetting has required replaying stored exemplars from prior tasks, which is often impractical. By contrast, language models can sample from their own training distribution, and we show that these self-generated samples serve as effective replay data, nearly eliminating forgetting. We find that forgetting nonetheless persists when the model has little remaining capacity: models pretrained close to saturation cannot absorb new information without overwriting prior knowledge. When capacity is not the limiting factor, low learning rates reduce forgetting but require substantially more training steps. Replay breaks this tradeoff, enabling fast, high-learning-rate finetuning without forgetting.