# Supplementary Materials

Folders:

- `grad_surgery`: IN-RIL implementation for full network fine-tuning. We adopt gradient surgery as the gradient separation mechansim. The codebase includes DPPO and IDQL integration with IN-RIL. A separate README is included for detailed instructions.
- `residual`: IN-RIL implementation for partial network fine-tuning using network separation for gradient separation. A separate README is included for detailed instructions.
- `demo_videos`: autonomous policy rollout demos on Robomimic and FurnitureBench tasks. Tasks such as `Transport` and `Lamp` are extremely challenging for RL agents due to their sparse rewards and long horizons.

Thank you for reviewing our paper and supplementary materials. We appreciate your feedback.

