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Reward functions are notoriously difficult to specify, especially for taskswith complex goals. Reward learning approaches attempt to infer rewardfunctions from human feedback and preferences. Prior works on reward learninghave mainly focused on the performance of policies trained alongside the rewardfunction. This practice, however, may fail to detect learned rewards that arenot capable of training new policies from scratch and thus do not capture theintended behavior. Our work focuses on demonstrating and studying the causes ofthese relearning failures in the domain of preference-based reward learning. Wedemonstrate with experiments in tabular and continuous control environmentsthat the severity of relearning failures can be sensitive to changes in rewardmodel design and the trajectory dataset composition. Based on our findings, weemphasize the need for more retraining-based evaluations in the literature.

On The Fragility of Learned Reward Functions