Before revision:  Finally, we evaluate our approach on several robotics-inspired simulated environments and empirically verify that the  \ method can successfully fine-tune in a sample-efficient way compared to existing baselines.
After revision:  Finally, we evaluate our approach on several robotics-inspired simulated environments and empirically verify that the  Ease-In-Ease-Out fine-tuning method can successfully fine-tune in a sample-efficient way compared to existing baselines.