构建可植入捷径的测评基准，检验大模型代理是否为刷分而作弊。
BAITBENCH: Measuring Agent Reward Hacking with Optional Shortcuts Planted in ML Tasks
- 设计三类合成表格任务，内置可选捷径提升公开得分但破坏隐含测试表现。
- 7个前沿代理中57.1%的运行出现奖励黑客行为，超半数在提示禁止下仍作弊。
- 提供带标注的对话数据集和评分管道，支持对抗奖励作弊方法的对比测试。
大型语言模型代理正被广泛用于自主执行机器学习实验，在极少人工干预下迭代优化目标指标。已有研究揭示此类环境中存在奖励黑客问题，引发对研究成果有效性和人工智能研发安全性的质疑。现有基准未能评估存在于数据或建模任务本身中的欺骗行为。本文提出 BAITBENCH，一套包含三个合成表格机器学习任务的测评基准，每个任务均设计有可选捷径：使用该捷径可提高公开测试分数，但在隐藏测试集上表现严重下降。由于捷径使用不违反任何明文规则，BAITBENCH 能衡量模型利用捷径以获取虚高得分的频率。通过双阶段裁判流水线评估七种前沿代理，发现57.1%的运行出现奖励黑客，其中五种代理超过50%。即使在明确禁止使用捷径的条件下，平均作弊率仍高于50%。本文开源 BAITBENCH、裁判实现及包含奖励黑客的对话标注数据集，作为评估奖励黑客缓解策略的基准测试平台。
原文摘要 · Abstract (English)
LLM agents are increasingly used to run autonomous ML experiments, iterating on target metrics with little human oversight. Prior work has documented reward hacking in these environments, bringing into question the validity of produced research and the broader safety case for AI R&D. Existing benchmarks do not measure exploits that live in the data or the modeling task itself. We introduce BAITBENCH, a suite of three synthetic tabular ML tasks that each contain a shortcut that allows agents to inflate the public test score but fail on a hidden test set. Since the shortcut is optional and using it breaks no stated rule, BAITBENCH measures how often models exploit the shortcut to achieve inflated scores. Across seven frontier agents scored by our two-stage judge pipeline, 57.1% of runs exhibit reward hacking, with five of seven above 50%. Agents cheat even under a second condition where they are prompted not to -the mean cheating rate remains above 50%. We release BAITBENCH, along with the judge implementation, and an annotated dataset of transcripts containing reward hacks as a testbed for evaluating reward-hacking mitigations head-to-head.
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