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--- |
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license: mit |
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dataset_info: |
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features: |
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- name: prompt |
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dtype: string |
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- name: completion |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 13449668588 |
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num_examples: 500000 |
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download_size: 3251708048 |
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dataset_size: 13449668588 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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task_categories: |
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- text-generation |
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tags: |
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- nethack |
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- interactive decision-making |
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- llm agents |
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- imitation learning |
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- behavioral cloning |
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--- |
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# LangHack |
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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). |
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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. |
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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. |
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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). |
