datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Zebra-CoT
Zebra‑CoT
A diverse large-scale dataset for interleaved vision‑language reasoning traces.
Dataset Description
Zebra‑CoT is a diverse large‑scale dataset with 182,384 samples containing logically coherent interleaved text‑image reasoning traces across four major categories: scientific reasoning, 2D visual reasoning, 3D visual reasoning, and visual logic & strategic games.
Dataset Structure
Each example in Zebra‑CoT consists of:
Problem statement:… See the full description on the dataset page: https://huggingface.co/datasets/multimodal-reasoning-lab/Zebra-CoT.ZebraLogicPaper: https://huggingface.co/papers/2502.01100
Arxiv: https://arxiv.org/abs/2502.01100
Citation
@article{zebralogic2025,
title={ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning},
author={Bill Yuchen Lin and Ronan Le Bras and Kyle Richardson and Ashish Sabharwal and Radha Poovendran and Peter Clark and Yejin Choi},
year={2025},
url={https://arxiv.org/abs/2502.01100},
}
@article{dziri2024faith,
title={Faith and fate: Limits of transformers on… See the full description on the dataset page: https://huggingface.co/datasets/WildEval/ZebraLogic.ZebraLogicBenchtextlatent_zebra_thinkmorph_armAB
Text-Latent (Arm A) vs All-Latent (Arm B) — Zebra-CoT + ThinkMorph
35638 samples/arm, 18 categories. Schema = ULVR/williamium style (sample_id, category, source_dataset,
question, answer, input_image, intermediate_image_N, num_intermediate_steps, messages_json).
armA_text_latent: real decoded text CoT + latent visual blocks (intermediate_image_1..3).
armB_render_latent: reasoning text RENDERED to images, all-latent baseline (intermediate_image_1..17).
messages_json = full Monet… See the full description on the dataset page: https://huggingface.co/datasets/RuoliuYang/textlatent_zebra_thinkmorph_armAB.multi-zebra-logic
Dataset Card for the MultiZebraLogic dataset
This dataset includes zebra puzzles in 39 European and 5 non-European languages and in two sizes: 2x3 and 4x5. It can be used for evaluating logical reasoning ability.
The data has been generated using the code in this repo.
Dataset Details
Dataset Description
Zebra puzzles are a type of constraint satisfaction problem. They describe a number of objects, N_objects, that each have attributes… See the full description on the dataset page: https://huggingface.co/datasets/alexandrainst/multi-zebra-logic.zebra-cot-mistral-small-3.2-24b-preprocessed
Zebra-CoT Preprocessed — Mistral Hackathon 2026
Preprocessed version of the Zebra-CoT dataset for fine-tuning Mistral-Small-3.2-24B-Instruct.
Format
text: formatted as [INST] question [/INST] <think> reasoning </think> answer
image: PIL JPEG image for the corresponding visual task
Usage
Fine-tuning Mistral-Small-3.2-24B on chain-of-thought visual reasoning.
Hackathon
Created for Mistral Hackaton 2026 — Fine-tuning track with W&B.
ZebraLogicBench-privateZebra-CoT-unify-stylezebra-puzzlesSynthetic data for the paper [2505.05755] Insertion Language Models: Sequence Generation with Arbitrary-Position Insertions.
Project page: https://dhruveshp.com/projects/ilm
ZebraArena
ZebraArena
Dataset accompanying the paper ZebraArena: A Diagnostic Simulation Environment for Studying Reasoning–Action Coupling in Tool-Augmented LLMs.
ZebraArena is a procedurally generated diagnostic environment for studying reasoning–action coupling in tool-augmented LLMs, with controllable difficulty and a knowledge-minimal design. Each task is a partially observed Zebra (logic-grid) puzzle: a Constraint Satisfaction Problem with a unique ground-truth solution, where a subset… See the full description on the dataset page: https://huggingface.co/datasets/ZebraArena/ZebraArena.rg-zebra_puzzles-instruct-100k
RLVR generated dataset
Procedural rows from reasoning-gym, formatted for verl GRPO.
Build metadata
{
"config": "/home/owais/Projects/rlvr/rlvr/configs/datasets/zebra_puzzles-instruct.yaml",
"template_type": "qwen-instruct",
"developer_prompt": null,
"data_source": "reasoning_gym",
"default_extract": "answer_tag",
"train_rows": 100000,
"test_rows": 4096,
"train_seed": 42,
"test_seed": 43,
"tasks": {
"zebra_puzzles": {
"weight": 1… See the full description on the dataset page: https://huggingface.co/datasets/carbonteq/rg-zebra_puzzles-instruct-100k.Zebrafish_DNA_v0rlvr_logic__zebra_puzzle_1.3klogic__zebra_puzzle_dataset_200Puzzle_Zebra_20Kzebra-puzzles-sortedPuzzle_Zebra_Allzebra_100
Zebra 100
Overview
This synthetic micro-dataset contains 177 pure logic deduction traces (5x5 zebra puzzles), consisting of 100 training examples and 77 validation examples. It features strictly logic grid puzzles and includes zero mathematical data.
The 100 training examples were randomly sampled without replacement from the train split of tamewild/instruct5, while the 77 validation examples are kept exactly the same across both datasets.
It was created to… See the full description on the dataset page: https://huggingface.co/datasets/tamewild/zebra_100.zebra-giraffe-imbalancedZebraArena
ZebraArena
ZebraArena dataset.
Configs missing1 … missing7 correspond to the number of missing clues; splits small / medium / large correspond to puzzle size.
Usage
from datasets import load_dataset
ds = load_dataset("WanjiaAlicia/ZebraArena", "missing3", split="large")
Puzzle_ZebraZebraLogicNoiseZebrafish-AChE-Orientation-ClassificationPuzzle_Zebra_20K_completionszebra_6zebra-giraffe-9000-swappedzebrazebra-giraffe-mix-captionssynthetic-zebra-giraffe-02ultra-short-prompt-collection-v0.1