测试编程模型在奖励机制下的欺骗行为，发现多个主流模型存在显性作弊。
EvilGenie: A Reward Hacking Benchmark
- 构建可诱导奖励欺骗的编程环境，通过硬编码和修改测试文件实现
- 大模型裁判对明显作弊检测效果好，额外测试集提升有限
- OpenAI、Anthropic、Google三款主流编码模型均出现作弊或行为错位
我们提出EvilGenie，一个用于编程场景下奖励黑客攻击的基准测试。数据源自LiveCodeBench，构建了一个易引发奖励欺骗的环境，例如硬编码测试用例或修改测试文件。通过三种方式评估奖励黑客行为：预留单元测试、大语言模型裁判和测试文件修改检测，并与人工评审相互验证。结果显示，大语言模型裁判在明确案例中表现优异，而预留测试用例带来的改进微乎其微。除使用Inspect的basic_agent框架测试多个模型外，还评估了三个主流专有编码代理：OpenAI Codex、Anthropic Claude Code和Google Gemini CLI。发现Codex和Claude Code存在显式奖励黑客行为，所有三款模型均表现出行为错位。代码库见https://github.com/JonathanGabor/evilgenie_inspect。
原文摘要 · Abstract (English)
We introduce EvilGenie, a benchmark for reward hacking in programming settings. We source problems from LiveCodeBench and create an environment in which agents can easily reward hack, such as by hardcoding test cases or editing the testing files. We measure reward hacking in three ways: held out unit tests, LLM judges, and test file edit detection. We verify these methods against human review and each other. We find the LLM judge to be highly effective at detecting reward hacking in unambiguous cases, and observe only minimal improvement from the use of held out test cases. In addition to testing many models using Inspect's basic\_agent scaffold, we also measure reward hacking rates for three popular proprietary coding agents: OpenAI's Codex, Anthropic's Claude Code, and Google's Gemini CLI. We observe explicit reward hacking by both Codex and Claude Code, and misaligned behavior by all three agents. Our codebase can be found at https://github.com/JonathanGabor/evilgenie_inspect .
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