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