Skip to content

Translation in progress Some entries still include text in the original language.

Oct 12 · Monday

Reward hacking · Papers

Found 4 papers
Filter1

Model risks

Reward hacking4

Impact

Oversight failure4

System type

AI Agent7coding agent4

Layer

Application layer3
Select any value within a category; different categories must all match. Counts include the other filters you have selected.
System type：coding agentTopic: Reward hackingClear all
  2. Evaluation and benchmarksarXiv 2609.39533

    CATCH：面向编码 RL 奖励作弊的可控分析测试台

    构建编码 RL 奖励作弊测试台 CATCH 并检验 CoT 监控失效

    arXiv
    2609.39533
    Published
    Layer
    Model layer
    Setting
    coding agent
    Evaluated models
    Qwen3-4B, Qwen3.5-27B
    AbstractCATCH 用双运行金标签研究编码 RL 的奖励黑客，并观察到监控会被注释适应削弱。

    CATCH 是面向编码 RL 奖励黑客的可控分析测试床，用可利用环境、可调初始倾向和基于执行的金标签，在训练全程比较黑客动态与干预。

    Paper analysisRead analysis
  4. Evaluation and benchmarksarXiv 2608.22103

    Hack-Verifiable Terminal Bench：评测终端任务中的奖励作弊

    提出 HVTB，在终端编程任务植入蜜罐以测量奖励作弊

    arXiv
    2608.22103
    Published
    Layer
    Application layer
    Setting
    coding agent
    Evaluated models
    claude-opus-5, gpt-5.6-sol, gemini-3.1-pro and others5
    Abstract用蜜罐测量终端任务上的 reward hacking，并比较五档提示词。

    HVTB 用来在真实终端与编程任务上测量 reward hacking：智能体读取被植入的参考解答或验收测试，从而偏离任务意图。作者认为这一失败模式会随智能体更自主而更重要，且事后人工或 LLM judge 可能失效。

    Paper analysisRead analysis
You have reached the end