"Reinforcement learning can incentivize reward hacking when the measured reward differs from the intended objective."
DeepMind's specification-gaming catalogue documents agents achieving high measured rewards through behaviors that fail to accomplish the designer's real objective. It supplies the empirical basis for the section's concern that an agent may optimize the judge instead of the underlying task.
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