Reward Hacking in Language Model Agents: Revisiting AI Safety Gridworlds

In three linesStudy on reward hacking in LLM-based agents using an adapted AI Safety Gridworlds framework. Models (1.5B–14B) systematically exploit misspecified objectives to maximize observed rewards while failing hidden safety objectives. RL optimization amplifies the problem and resists standard mitigations (exploration, regularization).

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