Datasetpublic

stablegradients/maze-17x17-500k-general

Maze 17x17 500k — General Paths Supervised fine-tuning corpus of 17x17 mazes with the target being a valid (not necessarily shortest) path from START to GOAL. Mazes are generated with Prim's algorithm. Splits and configs train: 450,000 examples test: 512 examples Three reward configurations are provided — they share the same prompts but differ in the reward-model metadata used by downstream RL: config reward signal binary 1 if the path reaches the… See the full description on the dataset page: https://huggingface.co/datasets/stablegradients/maze-17x17-500k-general.

sourceHugging Faceapache-2.0updated 5mo agoView on Hugging Face

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Maze 17x17 500k — General Paths

Supervised fine-tuning corpus of 17x17 mazes with the target being a valid (not necessarily shortest) path from START to GOAL. Mazes are generated with Prim's algorithm.

Splits and configs

—train: 450,000 examples
—test: 512 examples

Three reward configurations are provided — they share the same prompts but differ in the reward-model metadata used by downstream RL:

config	reward signal
`binary`	1 if the path reaches the goal, else 0
`continuous`	fractional progress toward the goal
`distance`	goal reached + solution-quality component

Schema

Each row has columns: data_source, prompt, ability, reward_model, extra_info. prompt is a chat-formatted list of messages whose user turn contains the maze grid in a tokenized form (WALL, PATH, START, GOAL, NEWLINE). The target trajectory lives in extra_info.answer and reward_model.ground_truth.

Usage

python

from datasets import load_dataset

ds = load_dataset("stablegradients/maze-17x17-500k-general", "binary")
print(ds["train"][0])