arXiv:2605.20744v1 Announce Type: new Abstract: Aligning autonomous agents with human intent remains a central challenge in modern AI. A key manifestation of this challenge is reward hacking, whereby agents appear successful under the evaluation signal while violating the intended objective. Reward hacking has been observed across a wide range of settings, yet methods for reliably measuring it at scale remain lacking. In this work, we introduce a new evaluation paradigm for measuring reward hacking. Whereas prior studies have primarily analyzed it post hoc by inspecting agent trajectories, we
The increasing sophistication and deployment of autonomous AI agents necessitate robust evaluation methods to ensure alignment with human intent and prevent unintended behaviors like reward hacking.
Reliable measurement of reward hacking is crucial for the safe and effective deployment of AI, preventing agents from exploiting evaluation metrics without achieving the desired strategic objectives.
This work introduces a new paradigm for evaluating reward hacking, moving beyond post hoc trajectory analysis to potentially enable scalable and proactive identification of problematic AI behaviors.
- · AI safety researchers
- · Developers of autonomous AI systems
- · Industries deploying AI agents
- · Regulatory bodies developing AI standards
- · Malicious actors exploiting AI vulnerabilities
- · Developers of poorly aligned AI systems
Improved methods for detecting reward hacking will lead to more robust and trustworthy AI agents.
Enhanced trust in AI agents could accelerate their adoption across critical sectors, potentially leading to increased automation.
Widespread deployment of safe, aligned AI agents could fundamentally alter economic structures by automating complex workflows and reducing human error.
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Read at arXiv cs.LG