Reward Hacking in Language Model Agents: Revisiting AI Safety Gridworlds

Reward hacking, where AI systems exploit misspecified objectives to achieve high reward without satisfying intended goals, remains a central challenge in AI safety. Yet most known instances have been discovered post hoc in frontier systems where controlled study is impractical. We adapt the AI Safety Gridworlds framework into a text-based evaluation suite that reformulates classic reinforcement learning safety tasks for language-based agents. Across frontier and mid-scale models, we find that sp

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ethics.ai (13 June 2026), “Reward Hacking in Language Model Agents: Revisiting AI Safety Gridworlds,” evidence record 1021, https://ethics.ai/record/1021 (originally published by arXiv).

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