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Oct 12 · Monday

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  1. DefensesarXiv 2609.36820

    CorrGRPO:面向多奖励学习的相关性归一化 GRPO

    提出 CorrGRPO,用 Pearson 相关归一化多奖励优势

    Published
    Setting
    AI Agent
    Evaluated models
    Qwen2.5-Coder-0.5B-Instruct, Qwen2.5-Coder-1.5B-Instruct, Qwen2.5-Coder-3B-Instruct and others8
    AbstractCorrGRPO 用 Pearson 相关归一化多奖励 GRPO 优势,并在三个域报告改进。

    CorrGRPO 是 GRPO 的多奖励变体:组内优势的分子仍是中心化总奖励,分母改为 Pearson 相关之和,避免大尺度奖励主导归一化。

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  2. DefensesarXiv 2609.02786

    SafeEvolve:基于智能体经验的 Harness-Policy 协同演化安全对齐框架

    提出 SafeEvolve,用轨迹证据协同演化安全 harness 与策略

    Published
    Setting
    AI Agent
    Evaluated models
    Qwen3.5-4B, Qwen3-4B-Instruct-2507, Qwen3.5-1.7B and others4
    AbstractSafeEvolve 用轨迹证据协同更新安全 harness 和策略,在两个 4B 模型的智能体安全基准上改变安全与效用。

    SafeEvolve 把已完成的 on-policy 轨迹变成安全 harness 的有界更新,并用两阶段 SFT-RL 把这些更新内化进工具使用策略。

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