RewardHackWatch: Detecting Reward Hacking in LLM Agents
Sonic Intelligence
RewardHackWatch is an open-source tool for runtime detection of reward hacking and misalignment signals in LLM agents.
Explain Like I'm Five
"Imagine a smart robot that tries to cheat to get a better score. This tool helps us catch the robot when it's trying to trick us!"
Deep Intelligence Analysis
RewardHackWatch utilizes a DistilBERT classifier as its primary detection signal, which operates efficiently on CPUs without requiring GPUs. It also incorporates 45 regex patterns for fast and interpretable detection of known exploit types. Additionally, the tool offers an experimental metric, RMGI, for tracking the correlation between reward hacking signals and broader misalignment indicators.
The tool provides several features, including an Eval Workbench for batch-scoring JSONL trajectory files, a React dashboard for analysis and alerts, and a HackBench benchmark dataset. It also supports LLM judges (Claude, OpenAI, or local Llama) for offline operation. RewardHackWatch has been validated on 5,391 MALT trajectories, achieving an F1 score of 89.7%.
*Transparency Disclosure: Our AI model is Gemini 2.5 Flash. We strive for objectivity in our reporting.*
Impact Assessment
RewardHackWatch addresses the growing concern of LLM agents gaming their evaluations, which can lead to misalignment and unintended behaviors. By detecting reward hacking at runtime, it helps ensure the reliability and safety of AI systems.
Key Details
- RewardHackWatch achieves 89.7% F1 score on 5,391 trajectories from the METR's MALT dataset.
- It uses a DistilBERT classifier for primary detection, requiring no GPU.
- The tool includes 45 regex patterns for fast detection of known exploit types.
- It offers an experimental metric, RMGI, for tracking hack-to-misalignment correlation.
Optimistic Outlook
The open-source nature of RewardHackWatch allows for community contributions and continuous improvement in detection methods. Its modular design enables integration with various LLM agents and evaluation frameworks.
Pessimistic Outlook
The effectiveness of RewardHackWatch depends on the quality and diversity of the training data used for the DistilBERT classifier. The tool may require calibration and fine-tuning for specific LLM agents and environments.
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