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Oct 12 · Monday

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Models and APIs24coding agent0Browser and computer use0Embodied systems and robotics1

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Application layer5Training and data layer1
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System type：coding agentSystem type：Browser and computer useSystem type：Embodied systems and roboticsLayer：Training and data layerClear all

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  1. Analysis and theoryarXiv 2610.03196

    研究：人形机器人学习流程四层代理均存在偏离，不止奖励作弊

    论证人形机器人学习四层代理可在目标缺失时报成功

    arXiv
    2610.03196
    Published
    Layer
    Training and data layer
    Setting
    Embodied systems and robotics
    Evaluated models
    Gaussian MLP（三隐层各 128 ELU，独立 critic，PPO）
    Abstract四层都可能在目标缺失时报告成功。

    作者讨论腿式机器人 RL 流水线中，奖励、课程门控、评测统计量和参考运动如何在物理目标缺失时仍报告成功。传统观点只把奖励当作会被优化、因而会偏离的代理。

    Paper analysisRead analysis
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