https://auto-driving-blog.vercel.app/tags/-%E6%A2%AF%E5%BA%A6%E6%A3%80%E6%9F%A5%E7%82%B9/ Recent content in 📦 梯度检查点 on Elon's AD Insight https://auto-driving-blog.vercel.app/images/share.png https://auto-driving-blog.vercel.app/images/share.png Hugo zh-cn Sun, 19 Jul 2026 00:00:00 +0000 https://auto-driving-blog.vercel.app/posts/knowledge/vla%E5%A4%A7%E6%A8%A1%E5%9E%8B%E8%AE%AD%E7%BB%83%E6%8A%80%E5%B7%A7/ Sun, 19 Jul 2026 00:00:00 +0000 https://auto-driving-blog.vercel.app/posts/knowledge/vla%E5%A4%A7%E6%A8%A1%E5%9E%8B%E8%AE%AD%E7%BB%83%E6%8A%80%E5%B7%A7/ 训练VLA模型远远不止’用DeepSpeed跑起来’这么简单，当模型规模从7B增长到70B参数时，显存瓶颈和通信开销会逼你深入理解每一个训练技巧的原理和trade-off。本文从混合精度训练（FP16 vs BF16 vs FP8的精度缩放策略）、梯度检查点（selective checkpointing vs full checkpointing vs recomputation profiling）、activation offloading（CPU offloading vs NVMe offloading）、序列并行（Ring Attention + sequence parallelism + context parallelism）四个核心技巧展开，配合VLA特有的多模态编码器训练优化，给出实用的训练配置建议与常见调优诊断方法。