Goal misgeneralization

"Goal Misgeneralization: Goal misgeneralization is another failure mode, wherein the agent actively pursuesobjectives distinct from the training objectives in deployment while retaining the capabilities it acquired duringtraining (Di Langosco et al., 2022). For instance, in CoinRun games, the agent frequently prefers reachingthe end of a level, often neglecting relocated coins during testing scenarios. Di Langosco et al. (2022) drawattention to the fundamental disparity between capability generalization and goal generalization, emphasizing howthe inductive biases inherent in the model and its training algorithm may inadvertently prime the model to learn aproxy objective that diverges from the intended initial objective when faced with the testing distribution. It impliesthat even with perfect reward specification, goal misgeneralization can occur when faced with distribution shifts(Amodei et al., 2016)."

"Goal or objective misgeneralization is a type of robustness failure where an AI system appears to be pursuing the intended objective in training, but does not generalize to pursuing this objective in out-of-distribution settings in deployment while maintaining good deployment performance in some tasks [180, 59]."