We present Reinforcement Learning via Auxiliary Task Distillation (AuxDistill); a new method for leveraging reinforcement learning (RL) in long-horizon robotic control problems by distilling behaviors from auxiliary RL tasks. AuxDistill trains pixels-to-actions policies end-to-end with RL, without demonstrations, a learning curriculum, or pre-trained skills. AuxDistill achieves this by concurrently doing multi-task RL in auxiliary tasks which are easier than and relevant to the main task. Behaviors learned in the auxiliary tasks are transferred to solving the main task through a weighted distillation loss. In an embodied object-rearrangement task, we show AuxDistill achieves 27% higher success rate than baselines.