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Datasets:
peppery77
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rpopsd

Tasks:
Visual Question Answering
Image-Text-to-Text
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Languages:
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Size:
1K - 10K
Tags:
multimodal
vision-language-model
visual-question-answering
fine-grained-perception
on-policy-distillation
resolution
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metadata 
license: apache-2.0
language:
  - en
task_categories:
  - visual-question-answering
  - image-text-to-text
size_categories:
  - 1K<n<10K
pretty_name: RP-OPSD Training Dataset
tags:
  - multimodal
  - vision-language-model
  - visual-question-answering
  - fine-grained-perception
  - on-policy-distillation
  - resolution

RP-OPSD Training Dataset

RP-OPSD is the complete 5,295-example multimodal training set used for the reported Dataset4.0 experiments. It pairs each prompt with two views of the same public source image:

  • images: the student view, resized to half the original width and height with Lanczos interpolation;
  • teacher_images: the original full-resolution teacher view.

The dataset contains 5,205 multiple-choice questions and 90 short-answer questions. It does not contain model rollouts, teacher/student model outputs, system prompts, credentials, or machine-specific paths.

Dataset summary

Item Count
Total examples 5,295
Multiple-choice questions 5,205
Short open questions 90
Explicit half-resolution failures (tier A) 5,115
Truncated or unparsed half-resolution failures (tier C) 180

Direct upstream datasets

Upstream dataset Pinned revision Selected examples
LMMs-Lab-Turtle/Vision-SR1-47K 2900b038f4aaa72f6b92795c1ee3ab29b7d509b6 887
UCSC-VLAA/VLM-CapCurriculum-Perception-Data 9f26e8d4e1b1c9d5bb53989cee4755c07f8ebb84 314
inclusionAI/ZwZ-RL-VQA fec404cb7bd5a18458e5add2802a5fb183981d14 3,034
yuanqianhao/Vision-OPD-6K eb5c1c2e7b9a7b6a619efe4161c7369c71bf8af4 1,060

All four direct upstream Hugging Face repositories identify their released datasets as Apache-2.0. See NOTICE for attribution and a description of the modifications made in this release.

Download

Download the complete repository snapshot, then extract the packaged images. images.tar contains the original assets/student/... and assets/teacher/... paths referenced by train.parquet:

from huggingface_hub import snapshot_download

dataset_dir = snapshot_download(
    repo_id="peppery77/rpopsd",
    repo_type="dataset",
)
print(dataset_dir)

Extract the image shards:

cd /path/to/downloaded/rpopsd
tar -xf images.tar

Command-line equivalent:

hf download peppery77/rpopsd \
  --repo-type dataset \
  --local-dir rpopsd

load_dataset() alone is not the recommended download method because the Parquet file stores relative image paths. Downloading the full snapshot and extracting the packaged shards restores the required directory layout.

Files 

rpopsd/
├── train.parquet
├── images.tar         # extracts to assets/student/ and assets/teacher/
├── selection_manifest.jsonl
├── asset_manifest.jsonl
├── summary.json
├── NOTICE
├── LICENSE
├── IMAGE_SHA256SUM
└── SHA256SUMS

All paths stored in train.parquet are relative to the repository root after images.tar is extracted.

Schema

Field Type Description
data_source string Dataset4.0 training source identifier
prompt list of role/content structs User prompt; no system prompt is used
images list of image-path structs Half-resolution student image
teacher_images list of image-path structs Full-resolution teacher image
ability string Task/ability label
reward_model struct Rule-based style and ground-truth answer
extra_info struct Version, task, source, tier, and resolution metadata

Resolve image paths with the repository root:

from pathlib import Path
import pyarrow.parquet as pq

root = Path(dataset_dir)
row = pq.read_table(root / "train.parquet").slice(0, 1).to_pylist()[0]

student_image = root / row["images"][0]["image"]
teacher_image = root / row["teacher_images"][0]["image"]
answer = row["reward_model"]["ground_truth"]

Prompt format

Multiple-choice questions:

<image>
{question and choices}

Answer with the option's letter from the given choices.

Short-answer questions:

<image>
{question}

Answer the question directly.

No system prompt is used. Red-box instructions are not included.

Construction

The first component was selected from Vision-SR1, VLM-CapCurriculum Perception, and ZwZ-RL-VQA. Qwen3.5-9B was evaluated on the original image and on a version with both physical dimensions halved. Examples were retained when the original-image answer was correct and the half-resolution answer was explicitly wrong, truncated, or unparseable.

The second component was selected from Vision-OPD examples for which the teacher was correct and the student was wrong. Its released representation uses the public original image as the teacher view and a half-resolution copy as the student view, without red-box overlays or red-box instructions. One normalized duplicate was removed.

The ordered selection manifest has SHA-256:

9df2f6802d5ac46b394055e9e7ab971a381509bd38637ebe939420130e330837

Different image codec versions can produce different encoded bytes when the dataset is reconstructed, while preserving the selected examples, prompts, answers, and physical half-resolution relationship.

Integrity

Run the packaged checksums from the repository root:

shasum -a 256 -c SHA256SUMS
shasum -a 256 -c IMAGE_SHA256SUM

asset_manifest.jsonl records the relative path, dimensions, byte size, role, and SHA-256 of every teacher and student image.

Known limitation

The 1,060-example Vision-OPD component was originally selected under a red-box-conditioned source setting and then reconstructed with no-box inputs. The final no-box condition was not independently re-filtered. This is a reported property of Dataset4.0, not a requirement of the RP-OPSD method.

License and attribution

This release is distributed under the Apache License 2.0. A copy is provided in LICENSE. Attribution, pinned revisions, and modifications are recorded in NOTICE. Users should also review and comply with the terms and dataset cards of the cited upstream releases.

The dataset is provided without warranties or conditions of any kind. The names of upstream projects and authors do not imply endorsement of this release.

Citation

Please cite the RP-OPSD paper/repository and the upstream datasets used by the examples in your work. The canonical RP-OPSD BibTeX entry will be added here when the final paper citation is available.

中文说明

RP-OPSD 是 Dataset4.0 实验使用的完整多模态训练集，共 5,295 条样本，其中 5,205 条为选择题，90 条为短答案题。每条样本包含同一公开来源图片的两种视图：

  • images：宽高分别缩小为原始尺寸一半的 student 图片，使用 Lanczos 插值；
  • teacher_images：原始完整分辨率的 teacher 图片。

建议使用上面的 snapshot_download 或 hf download 下载完整仓库，然后执行 tar -xf images.tar；归档会恢复 assets/student/ 和 assets/teacher/，从而保持 train.parquet 中图片相对路径的对应关系。许可证、上游固定版本、署名与修改 说明分别见 LICENSE 和 NOTICE。