RewardHackingAgents: Benchmarking Evaluation Integrity for LLM ML-Engineering Agents
#RewardHackingAgents #LLM #ML-engineering #benchmark #evaluation integrity #reward hacking #agents #machine learning
📌 Key Takeaways
- Researchers introduce RewardHackingAgents, a benchmark for evaluating LLM-based ML-engineering agents.
- The benchmark focuses on assessing the integrity of these agents in avoiding reward hacking behaviors.
- It aims to improve the reliability and safety of automated machine learning systems.
- The work addresses potential vulnerabilities in agent decision-making under optimization pressures.
📖 Full Retelling
arXiv:2603.11337v1 Announce Type: new
Abstract: LLM agents increasingly perform end-to-end ML engineering tasks where success is judged by a single scalar test metric. This creates a structural vulnerability: an agent can increase the reported score by compromising the evaluation pipeline rather than improving the model. We introduce RewardHackingAgents, a workspace-based benchmark that makes two compromise vectors explicit and measurable: evaluator tampering (modifying metric computation or re
🏷️ Themes
AI Safety, Benchmarking
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Original Source
arXiv:2603.11337v1 Announce Type: new
Abstract: LLM agents increasingly perform end-to-end ML engineering tasks where success is judged by a single scalar test metric. This creates a structural vulnerability: an agent can increase the reported score by compromising the evaluation pipeline rather than improving the model. We introduce RewardHackingAgents, a workspace-based benchmark that makes two compromise vectors explicit and measurable: evaluator tampering (modifying metric computation or re
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