| | --- |
| | 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). |
