Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Incremental Learning of Retrievable Skills For Efficient Continual Task Adaptation
Authors: Daehee Lee, Minjong Yoo, Woo Kyung Kim, Wonje Choi, Honguk Woo
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 29,059 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,980 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate Is Ci L and several adapter-based continual learning baselines across scenario variations based on complex, long-horizon tasks in the Franka-Kitchen and Meta-World environments to assess sample efficiency, task adaptation, and privacy considerations. |
| Researcher Affiliation | Academia | Sungkyunkwan University Carnegie Mellon University EMAIL |
| Pseudocode | Yes | Algorithm 1 Is Ci L Skill Incremental Learning |
| Open Source Code | Yes | Yes, we provide the codes for supplementary material. |
| Open Datasets | Yes | To investigate the sample efficiency and adaptation performance, we construct complex Ci L scenarios using diverse long-horizon tasks [29, 30, 31]. |
| Dataset Splits | No | The paper mentions training and test data, but does not explicitly describe a validation dataset split or its specific use for hyperparameter tuning or early stopping. |
| Hardware Specification | Yes | Our experimental platform is powered by an AMD 5975wx CPU and 2x RTX 4090 GPUs. |
| Software Dependencies | Yes | We utilized jax 0.4.24, jaxlib 0.4.19, and flax 0.8.2 for our implementation. |
| Experiment Setup | Yes | Table 11: Pre-trained model configure and Table 12: Continual imitation learning default hyperparameters. |