Benchmarks for Lifelong In-Context Learning
Project description
L3C
L3C provides variant environments facilitating In-Context Learning, featured in the following assets:
- Vast amounts of diversified tasks with minimal inductive bias
- Lifelong In-Context Learning
- Generative and Interactive
Environments Updating
-
AnyMDP: Procedurally generated unlimited general-purpose Markov Decision Processes (MDP)
-
MetaLanguage: Pseudo-language generated from randomized neural networks
-
MazeWorld: Procedurally generated mazes with diverse maze structures, navigation goals to benchmark object navigation
-
L3C_Baselines: An implementation of the baseline solutions to the above tasks
Installation
pip install l3c
Reference
Cite this work with
@article{wang2024benchmarking,
title={Benchmarking General Purpose In-Context Learning},
author={Wang, Fan and Lin, Chuan and Cao, Yang and Kang, Yu},
journal={arXiv preprint arXiv:2405.17234},
year={2024}
}
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
l3c-0.2.1.3.tar.gz
(1.3 MB
view hashes)
Built Distribution
l3c-0.2.1.3-py3-none-any.whl
(1.3 MB
view hashes)