README.md

---
pretty_name: OSReward Data
task_categories:
- image-to-text
- reinforcement-learning
language:
- en
configs:
- config_name: sft
  data_files:
  - split: train
    path: sft/viewer/*.parquet
  default: true
- config_name: rl
  data_files:
  - split: train
    path: rl/train.parquet
  - split: validation
    path: rl/val.parquet
---

# OSReward Data

OSReward is a multimodal reward-model dataset for judging whether GUI-agent
trajectories complete the user's task. The release contains:

- Supervised fine-tuning data in a LLaMAFactory-compatible ShareGPT layout.
- Deduplicated screenshots packaged in independently extractable tar shards.
- Training and validation data for GRPO.
- The rule reward and a concise reference configuration for the RL experiment.

The expected answer contains a brief evidence-based analysis followed by a
final line in exactly one of these forms:

```text
Judge: SUCCESS
Judge: FAIL
```

## Layout

```text
sft/datasets/                 SFT JSON files
sft/viewer/                   Parquet view of the SFT data for Hub preview
sft/images-shards/            independent image tar shards
sft/dataset_info.json         LLaMAFactory dataset entries
rl/train.parquet              GRPO training split
rl/val.parquet                GRPO validation split
rl/osreward_reward.py         rule reward
stats/                        compact release statistics
```

## Quick Start

Extract all screenshots from the repository root:

```bash
bash sft/extract_images.sh
```

This restores paths under `osreward_rm_train_bundle/images/`, matching the
relative image paths used by both SFT and RL records.

The SFT JSON files are the canonical LLaMAFactory training files. The Parquet
files under `sft/viewer/` contain the same records in a format supported by
the Hub dataset preview.

See [DATA_CARD.md](DATA_CARD.md), [sft/SFT_FORMAT.md](sft/SFT_FORMAT.md), and
[rl/RL_FORMAT.md](rl/RL_FORMAT.md) for schemas and limitations.

## Integrity

Verify the downloaded release with:

```bash
sha256sum -c SHA256SUMS
```

No model weights or training framework checkout is included.