TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models
Authors: Zuxin Liu, Jesse Zhang, Kavosh Asadi, Yao Liu, Ding Zhao, Shoham Sabach, Rasool Fakoor
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experiments comparing prevalent parameter-efficient fine-tuning techniques and adaptation baselines suggest that TAIL with Lo RA can achieve the best post-adaptation performance with only 1% of the trainable parameters of full fine-tuning while avoiding catastrophic forgetting and preserving adaptation plasticity in continual learning settings. |
| Researcher Affiliation | Collaboration | 1Carnegie Mellon University, 2 University of Southern California, 3 Amazon Web Services |
| Pseudocode | No | The paper includes architectural diagrams and mathematical equations, but no explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement or link indicating that the authors are releasing their source code for the methodology described in the paper. |
| Open Datasets | Yes | We utilize the LIBERO robotic manipulation continual learning benchmark (Liu et al., 2023a), which features a diverse range of tasks... |
| Dataset Splits | Yes | Each task provides 50 successful human demonstrations. These are divided into 40 training trajectories and 10 for validation. |
| Hardware Specification | Yes | Our experimental platform was powered by an AMD EPYC 7R32 CPU running Ubuntu 20.04.06. All trainings utilized 8 NVIDIA A10G GPUs, each with a memory of 22731 Mi B, equipped with driver version 470.199.02 and CUDA version 11.4. |
| Software Dependencies | Yes | Our experimental platform was powered by an AMD EPYC 7R32 CPU running Ubuntu 20.04.06. All trainings utilized 8 NVIDIA A10G GPUs, each with a memory of 22731 Mi B, equipped with driver version 470.199.02 and CUDA version 11.4. |
| Experiment Setup | Yes | The environment configuration and the temporal decoder (GPT-2) hyperparameters are presented in Table 4. The detailed hyperparameters are presented in Appendix B. |