Datasets:
license: mit
dataset_info:
features:
- name: prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 13449668588
num_examples: 500000
download_size: 3251708048
dataset_size: 13449668588
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
task_categories:
- text-generation
tags:
- nethack
- interactive decision-making
- llm agents
- imitation learning
- behavioral cloning
LangHack
LangHack is a dataset of diff history demonstration data for the rogue-like video game NetHack generated using the symbolic AutoAscend bot, which boasts state-of-the-art performance in the game (as of 07/22/2024).
This dataset was created by sub-sampling 10,000 full NetHack games played by AutoAscend into contiguous "chunks" of 64 timesteps, and converting the agent's game state observations in natural language text using the NetHack Language Wrapper. Sub-sampling was performed uniformly at random over all recorded game data.
LangHack prompts correspond to a full game state observation at one timestep of AutoAscend gameplay, while completions correspond to a interleaved set of the subsequent bot actions and their resultant text deltas in the world state.
A detailed report of NetHack agent performance achieved by finetuning a tiny LLM (GPT2-127M) on LangHack is provided here.