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      <title>Flow-GRPO 完全讲解：训练/推理/梯度流/Loss 设计的逐行拆解</title>
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      <description>从 train_flux_fast.py 第 1 行开始，逐层追踪 Flow-GRPO 的完整逻辑链：采样阶段做了什么？reward 怎么变成 advantage？训练阶段的计算图是怎么构造的？loss 为什么那样设计？梯度如何从最后一个 log_prob 传到 LoRA 参数？每段代码都标注了源文件行号。</description>
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