Deep reinforcement learning~(RL) has achieved remarkable successes in complex single-task settings. However, designing RL agents that can learn multiple tasks and leverage prior experience to quickly adapt to a related new task remains challenging. Despite previous attempts to improve on these areas, our understanding of multi-task training and generalization in RL remains limited. To fill this gap, we investigate the generalization capabilities of a popular actor-critic method, IMPALA. Specifically, we build on previous work that has advocated for the use of modes and difficulties of Atari 2600 games as a challenging benchmark for transfer learning in RL. We do so by pretraining an agent on multiple variants of the same Atari game before fine-tuning on the remaining never-before-seen variants. This protocol simplifies the multi-task pretraining phase by limiting negative interference between tasks and allows us to better understand the dynamics of multi-task training and generalization. We find that, given a fixed amount of pretraining data, agents trained with more variations are able to generalize better. Surprisingly, we also observe that this advantage can still be present after fine-tuning for 200M environment frames than when doing zero-shot transfer. This highlights the potential effect of a good learned representation. We also find that, even though small networks have remained popular to solve Atari 2600 games, increasing the capacity of the value and policy network is critical to achieve good performance as we increase the number of pretraining modes and difficulties. Overall, our findings emphasize key points that are essential for efficient multi-task training and generalization in reinforcement learning.