节前的某天，数据集预览服务出现了一次 OOM（内存溢出）问题。这类问题放在过去，其实是比较消耗时间的。 数据集预览涉及多种格式解析：jsonl、csv、parquet、json 等，每种格式的读取方式、内存占用模型都不一样。要逐个排查内存增长点，分析数据加载策略、对象生命周期以及是否存在全量读入等问题，通常至少需要 1 天时间。
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。关于这个话题，服务器推荐提供了深入分析
作为 RLHF 方面的专家，Lambert 认为，当前最顶尖的模型训练，已经高度依赖强化学习（RL）。而 RL 和蒸馏在本质上是两种不同的事情：
The second approach offers broader feature support, seen in projects like Cloud Hypervisor or QEMU microvm. Built for heavier and more dynamic workloads, it supports hot-plugging memory and CPUs, which is useful for dynamic build runners that need to scale up during compilation. It also supports GPU passthrough, which is essential for AI workloads, while still maintaining the fast boot times of a microVM.