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Oct 12 · Monday

Reward hacking · Papers

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  1. Risky behaviorarXiv 2606.04923

    CHERRL：可复现、分析与检测评分标准强化学习中的奖励作弊

    提出 CHERRL 注入裁判偏差以复现奖励作弊，并用 RHDA 检测训练起点

    arXiv
    2606.04923
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    Evaluated models
    Qwen3-4B, Qwen3.5-plus, Qwen3.5-397B-A17B and others4
    AbstractCHERRL 注入已知偏差以观察奖励黑客，并用于分析偏差和评测起点检测器 RHDA。

    CHERRL 是 rubric-based RL 的可控环境，用来复现奖励黑客、观察奖励分歧，并给检测器提供起点参照。

    Paper analysisRead analysis
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