The dataset was constructed through manual gameplay. At each step, given the system and user prompts describing the current game state, a corresponding use_tool action was explicitly authored by a human player while directly interacting with the environment. This process ensured that every action strictly followed the defined interface and playbook constraints.

In some cases, data augmentation was applied by modifying object names to account for variations across different situations.

All data was generated firsthand through direct gameplay and prompt–action annotation, without relying on external datasets or third-party sources. As a result, the dataset does not inherit any external licensing restrictions and can be freely used for research and experimentation purposes.

Run Instructions

from datasets import load_dataset

dataset = load_dataset("small-lit/overfit_small-aicrowd-pokemon-training-data", split="train")

# Dict로 변환 (기존 JSONL과 동일한 형식)
data = [dict(example) for example in dataset]
print(data[:2])  # 첫 2개 예시 출력

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