| --- |
| 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](https://diffhistory.github.io/) demonstration data for the rogue-like video game [NetHack](https://github.com/facebookresearch/nle) generated using the symbolic [AutoAscend bot](https://github.com/maciej-sypetkowski/autoascend), 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](https://github.com/ngoodger/nle-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](https://huggingface.co/openai-community/gpt2)) on LangHack is provided [here](https://arxiv.org/abs/2312.07540). |
