dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: ground_truth
dtype: string
- name: dataset
dtype: string
- name: env_config
struct:
- name: env_name
dtype: string
- name: image
dtype: string
- name: task_id
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 6001501
num_examples: 1552
download_size: 1375020
dataset_size: 6001501
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: apache-2.0
💻 Code · 🤗 Models & Data · 📜 Paper · 📓 Blog
For full information, go check out the Tmax paper here.
CLI Gym - Open Instruct formatted
This is cli-gym formatted for use with our fork of open-instruct. For more information and the original data, you should check out the original source.
Release collection: https://huggingface.co/collections/TMaxxx/tmax-v11
Source dataset: https://huggingface.co/datasets/allenai/open-instruct-cli-gym/tree/95deca8fabed214ece5c235bcc2ae675d878158e
Source and license
This is a tmax training-format conversion of the original dataset. The original authors' license and reuse terms apply to the source data; consult the upstream license and dataset documentation for those terms, attribution, and citation requirements. This conversion does not relicense the source tasks or bundled third-party materials.
Citation
If you found this data useful, please cite the original paper:
@misc{lin2026cligymscalableclitask,
title={CLI-Gym: Scalable CLI Task Generation via Agentic Environment Inversion},
author={Yusong Lin and Haiyang Wang and Shuzhe Wu and Lue Fan and Feiyang Pan and Sanyuan Zhao and Dandan Tu},
year={2026},
eprint={2602.10999},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2602.10999},
}