20260706:新增一些文章
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concepts/behavior-monitoring-rl.md
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title: "行为监控 RL(Behavior Monitoring in RL)"
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created: 2026-07-02
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updated: 2026-07-02
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type: concept
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tags: [verification, reward-hacking, rl, monitoring, coding-agent]
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sources:
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- "[[verification-horizon-no-silver-bullet]]"
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---
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# 行为监控 RL
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在 RL 训练中对 agent trajectory 进行审计,检测并惩罚 shortcut 行为的机制,是防御 [[reward-hacking|奖励破解]] 的关键手段。
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## 设计
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### Pattern Set `P`
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每个 pattern 指定三项:
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1. **Observable evidence**:轨迹中的可观察证据(命令历史、网络访问、git 操作)
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2. **Leakage risk**:关联的信息泄露风险
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3. **Intervention**:token 级惩罚
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### 闭环更新
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Pattern set 在训练过程中迭代更新:
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1. 每轮 RL 后,从当前 policy 抽样 trajectory
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2. Agentic reviewer 检查轨迹,发现新的 shortcut 策略
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3. 追加到 `P`,下一轮 RL 部署更新的 monitor
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**关键**:reward hacking 是 policy-dependent 的——新 shortcut 随模型提升而涌现,静态 pattern set 不足以覆盖。
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## 发现的两类泄露
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| 类型 | 行为 | 频率 | Resolved Rate |
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|------|------|------|:---:|
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| **静态环境泄露** | Repository-history mining | 3.69% | 47.29%(↓基线) |
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| | Test-oracle tampering | 8.25% | 41.47%(↓基线) |
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| **Policy 依赖捷径** | Solution artifact retrieval | 4.32% | 72.34%(↑12.35pp) |
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| | External fix lookup | 7.03% | 61.69%(↑1.70pp) |
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## 参考
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- [[verification-horizon-no-silver-bullet|论文原文]]
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- [[test-driven-rewards|测试驱动奖励]]
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- [[reward-hacking|奖励破解]]
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