这篇论文解决了LLM在持续学习中灾难性遗忘和可塑性丧失的痛点，做模型微调、持续学习或Agent长期记忆的团队值得关注——FST框架让你不用在参数更新和上下文学习之间二选一，直接结合两者优势。
这篇论文提出了一种名为Fast-Slow Training（FST）的框架，将LLM的参数视为“慢权重”，优化后的上下文视为“快权重”。快权重通过文本反馈吸收任务特定信息，慢权重则保持基础模型的一般推理能力。实验表明，FST在推理任务上比仅用强化学习（慢学习）样本效率提升3倍，且性能上限更高。FST训练的模型与基础LLM的KL散度降低70%，显著减少灾难性遗忘，并保持可塑性——在连续学习场景中，FST能持续获取新任务，而参数仅更新的RL方法会停滞。
Learning, Fast and Slow: Towards LLMs That Adapt Continually
Large language models (LLMs) are trained for downstream tasks by updating their parameters (e.g., via RL). However, updating parameters forces them to absorb task-specific information, which can result in catastrophic forgetting and loss of plasticity. In contrast, in-context learning with fixed LLM parameters can cheaply and rapidly adapt to task-specific requirements (e.g., prompt optimization), but cannot by itself typically match the performance gains available through updating LLM parameters. There is no good reason for restricting learning to being in-context or in-weights. Moreover, humans also likely learn at different time scales (e.g., System 1 vs 2). To this end, we introduce a fast-slow learning framework for LLMs, with model parameters as "slow" weights and optimized context as "fast" weights. These fast "weights" can learn from textual feedback to absorb the task-specific information, while allowing slow weights to stay closer to the base model and persist general reasoning behaviors. Fast-Slow Training (FST) is up to 3x more sample-efficient than only slow learning (RL) across reasoning tasks, while consistently reaching a higher performance asymptote. Moreover, FST-trained models remain closer to the base LLM (up to 70% less KL divergence), resulting in less catastrophic forgetting than RL-training. This reduced drift also preserves plasticity: after training on one task, FST trained models adapt more effectively to a subsequent task than parameter-only trained models. In continual learning scenarios, where task domains change on the fly, FST continues to acquire each new task while parameter-only RL stalls.