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  "title": "VLA换动作块不再卡顿",
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        "summary": "跟同期出现的'训练时RTC'(也在训练阶段引入衔接约束,但只是给重叠动作段加一个硬性前缀约束、不改变底层flow动力学本身)对比,Legato在任务分数、完成时间、平滑度上全面胜出——说明真正重要的是重塑策略本身的去噪动力学,不是单纯加一个训练时硬约束 validates 问题重述:RL 后训练的关键不只是「改多少」,更是「在哪一层介入」",
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        "paper_id": "flex_pi_a_multi_stream_world_action_model_with_compute_flexibility_2026_08",
        "title": "把RGB、3D几何、物体语义揉进同一个表征——这才是它比π0.5、Fast-WAM更准的根本原因",
        "url": "https://haiguangboy.com/posts/flex_pi_a_multi_stream_world_action_model_with_compute_flexibility",
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        "relation": "same_track",
        "summary": "动作分块(chunking)是VLA部署的标配,能摊薄推理成本、支持高频控制,但推理延迟加上flow策略内在的多模态性,导致相邻两个动作块衔接处经常不平滑,表现为犹豫和突兀转向,拖长任务完成时间 validates 部署:单次前向约 0.14 秒(超过 7Hz),预测 25 步动作块只执行前 8 步,RTC 式热启动加重叠混合平滑衔接",
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    "text": "\"训练时把部署期会变化的麻烦参数随机化、让策略自己适应\"这个手法，和已解读过的PointWorld同源——PointWorld随机化相机数量、这篇随机化推理延迟，结果都是模型对该参数的各种取值反而最鲁棒。调度消融里步幅和坡降长度的权衡，也和PAVE的预测锚点数量非单调发现是同一个模式：给系统更多约束或参数不是无条件的好事，关键在找到那个恰好的权衡点。"
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