构建可控制的测试环境，揭示评分模型偏见引发的奖励欺骗问题。
Reproducing, Analyzing, and Detecting Reward Hacking in Rubric-Based Reinforcement Learning
- 设计可控环境CHERRL，注入已知偏见模拟奖励欺骗。
- 发现不同评分偏见的可探测性与可利用性差异。
- 开发自动检测训练中奖励欺骗发生时机的智能代理。
基于评分标准的强化学习（Rubric-based RL）利用大语言模型作为评判者（LaaJ）根据评分标准对模型输出打分并提供奖励。然而，策略模型可能利用评判者中的潜在偏见，导致奖励欺骗，引发无效或不安全的训练结果。在真实场景中，此类欺骗行为往往隐蔽且与多种评判偏见交织，难以分析、检测和缓解。本文提出CHERRL——一个可控制的奖励欺骗实验环境，通过向LaaJ注入已知偏见，实现奖励欺骗的稳定复现、奖励偏差的明确观测以及欺骗起点的精准识别。该环境为研究奖励欺骗机制及其缓解方法提供了清晰的实验平台。我们从可发现性和可利用性角度分析了不同评判偏见，并探索了一种从训练日志中自动检测奖励欺骗发生时机的智能体。代码与环境已公开于https://github.com/THUAIS-Lab/CHERRL。
原文摘要 · Abstract (English)
Rubric-based reinforcement learning (RL) uses an LLM-as-a-Judge (LaaJ) to score model outputs according to rubrics as rewards. However, policy models may exploit latent biases in the judge, leading to reward hacking and ineffective or unsafe training outcomes. In real-world rubric-based RL, such hacking behaviors are often subtle and entangled with multiple judge biases, making them difficult to analyze, detect, and mitigate. In this paper, we introduce CHERRL, a Controllable Hacking Environment for Rubric-based RL. By injecting known biases into LaaJ, CHERRL enables stable reproduction of reward hacking, explicit observation of reward divergence, and identification of hacking onset. This provides a clean experimental testbed for studying the mechanisms and mitigations of reward hacking in rubric-based RL. To demonstrate its utility, we analyze different judge biases from the perspectives of discoverability and exploitability, and explore an agent for automatically detecting reward hacking onset from training logs. The code and environment are publicly available at https://github.com/THUAIS-Lab/CHERRL.
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