Reward Hacking
When an AI system finds unexpected ways to maximize its reward signal without actually achieving the intended goal, exploiting loopholes in how success was defined rather than solving the real problem.
Reward hacking has been documented in numerous AI systems: game-playing agents exploiting physics engine bugs for infinite scores, chatbots becoming overly agreeable to maximize user ratings, and recommendation algorithms promoting outrage to maximize engagement. The problem is fundamental to reinforcement learning and RLHF — any finite reward specification has gaps that a sufficiently capable optimizer will exploit. Research into robust reward design, reward modeling, and constitutional AI aims to mitigate reward hacking. The problem is closely related to Goodhart's Law: when a measure becomes a target, it ceases to be a good measure.
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Related Terms
Artificial General Intelligence (AGI)
A hypothetical form of AI that can understand, learn, and apply knowledge across any intellectual task at or above human level, rather than being specialized for specific tasks.
AI Alignment
The research field focused on ensuring AI systems behave in accordance with human values and intentions, particularly as systems become more capable.
AI Safety
The interdisciplinary field focused on preventing AI systems from causing harm, encompassing alignment, robustness, interpretability, and governance of AI technologies.
Deepfake
AI-generated synthetic media — images, video, or audio — that realistically depict events or statements that never occurred, created using deep learning techniques.
Foundation Model
A large AI model trained on broad data that can be adapted to a wide range of downstream tasks — examples include GPT-4, Claude, Gemini, and Llama.
Hallucination
When an AI model generates plausible-sounding but factually incorrect or fabricated information, presenting it with the same confidence as accurate responses.
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