通过可解释的任务分解，识别并减少具身智能体的奖励欺骗行为。
The Horcrux: Mechanistically Interpretable Task Decomposition for Detecting and Mitigating Reward Hacking in Embodied AI Systems
- 分层变压器架构将任务拆解为可解释子任务，提升过程透明度。
- 12至25步分解深度使四种失败模式下奖励欺骗率降低34%。
- 生成注意力瀑布图等可视化工具，帮助理解智能体决策路径。
具身智能体常通过利用奖励信号缺陷进行奖励欺骗，在获得高代理分数的同时偏离真实目标。本文提出机制可解释的任务分解（MITD），一种包含规划器、协调器和执行器模块的分层变压器架构，用于检测与缓解奖励欺骗。MITD在任务分解过程中生成可解释的子任务，并提供诊断性可视化，如注意力瀑布图与神经路径流图。在1,000个HH-RLHF样本上的实验表明，分解深度为12至25步时，四种失败模式下的奖励欺骗频率平均降低34%。结果揭示，基于机制的任务分解比事后行为监控更有效，为奖励欺骗检测提供了新范式。
原文摘要 · Abstract (English)
Embodied AI agents exploit reward signal flaws through reward hacking, achieving high proxy scores while failing true objectives. We introduce Mechanistically Interpretable Task Decomposition (MITD), a hierarchical transformer architecture with Planner, Coordinator, and Executor modules that detects and mitigates reward hacking. MITD decomposes tasks into interpretable subtasks while generating diagnostic visualizations including Attention Waterfall Diagrams and Neural Pathway Flow Charts. Experiments on 1,000 HH-RLHF samples reveal that decomposition depths of 12 to 25 steps reduce reward hacking frequency by 34 percent across four failure modes. We present new paradigms showing that mechanistically grounded decomposition offers a more effective way to detect reward hacking than post-hoc behavioral monitoring.
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