  Fine-tuning is a widespread technique that allows practitioners to transfer
pre-trained capabilities, as recently showcased by the successful applications
of foundation models. However, fine-tuning reinforcement learning (RL) models
remains a challenge. This work conceptualizes one specific cause of poor
transfer, accentuated in the RL setting by the interplay between actions and
observations: forgetting of pre-trained capabilities. Namely, a model
deteriorates on the state subspace of the downstream task not visited in the
initial phase of fine-tuning, on which the model behaved well due to
pre-training. This way, we lose the anticipated transfer benefits. We identify
conditions when this problem occurs, showing that it is common and, in many
cases, catastrophic. Through a detailed empirical analysis of the challenging
NetHack and Montezuma's Revenge environments, we show that standard knowledge
retention techniques mitigate the problem and thus allow us to take full
advantage of the pre-trained capabilities. In particular, in NetHack, we
achieve a new state-of-the-art for neural models, improving the previous best
score from $5$K to over $10$K points in the Human Monk scenario.
