README.md

---
dataset_info:
  features:
  - name: data_source
    dtype: string
  - name: prompt
    list:
    - name: role
      dtype: string
    - name: content
      dtype: string
  - name: ability
    dtype: string
  - name: reward_model
    struct:
    - name: style
      dtype: string
    - name: ground_truth
      dtype: string
  - name: extra_info
    struct:
    - name: index
      dtype: int64
  splits:
  - name: train
    num_bytes: 10737418240
    num_examples: 7861
  download_size: 10737418240
  dataset_size: 10737418240
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- reinforcement-learning
- text-generation
tags:
- code
- reasoning
- rlhf
- verl
---

# Code Contests Plus (VERL Format)

This dataset contains 8,432 competitive programming problems from the Code-Contests-Plus dataset, converted to VERL format for reinforcement learning applications. Each problem includes test cases validated through sandbox execution.

**Source**: [ByteDance-Seed/Code-Contests-Plus](https://huggingface.co/datasets/ByteDance-Seed/Code-Contests-Plus) (1x config)

**License**: MIT

## Dataset Structure

The dataset follows the VERL format with the following fields:

- `data_source` (string): Dataset source identifier ("code-contests-plus")
- `prompt` (list): Chat template format with role/content structure containing the coding problem
- `ability` (string): Task category ("code")
- `reward_model` (dict): Evaluation information
  - `style`: Evaluation method ("rule")
  - `ground_truth`: JSON-encoded test cases with input/output pairs
- `extra_info` (dict): Additional metadata
  - `index`: Example index from original dataset

## Test Case Format

Each problem includes test cases in the `reward_model.ground_truth` field, stored as JSON with the following structure:

```json
{
  "inputs": ["3\n1 2 3\n"],
  "outputs": ["6\n"]
}
```

The format consists of two parallel arrays:
- `inputs`: Array of input strings for each test case
- `outputs`: Array of expected output strings corresponding to each input

Each problem typically contains between 1 and 32 test cases, validated through sandbox execution during dataset creation.

## Data Processing

The dataset was created through a multi-step processing pipeline:

### 1. Test Case Extraction
- Extracted public test cases from the original dataset
- Validated format and executability
- Filtered problems without valid test cases

### 2. Sandbox Validation
- Each problem's test cases were validated using a sandbox environment
- Test input/output pairs verified for correctness
- Only problems with passing validation were included

### 3. Size Filtering
- Applied 10MB size limit to test case JSON (encoded)
- Removed overly large problems to ensure efficient processing
- Balanced dataset quality and usability

### Processing Statistics

- **Total input examples**: 11,690
- **Successfully processed**: 8,432 (72.1% success rate)
- **Total filtered**: 3,258 (27.9%)
  - No test cases: 54 (0.5%)
  - Size filtered (>10MB): 3,204 (27.4%)
- **Processing time**: 69 minutes
- **Configuration used**: 1x (standard difficulty)

## Usage

```python
from datasets import load_dataset
import json

# Load the dataset
dataset = load_dataset("sungyub/code-contests-plus-verl")

# Access an example
example = dataset['train'][0]

# Get the problem description
problem = example['prompt'][0]['content']
print("Problem:", problem)

# Parse test cases
ground_truth = json.loads(example['reward_model']['ground_truth'])
inputs = ground_truth['inputs']
outputs = ground_truth['outputs']

print(f"\nNumber of test cases: {len(inputs)}")
print(f"First input: {repr(inputs[0])}")
print(f"Expected output: {repr(outputs[0])}")
```

## Example Problem

**Problem Description:**
```
Twins

square1001 and E869120 are twins, but they are not identical twins...
```

**Test Case:**
```python
Input:  ""
Output: "square1001"
```

## Statistics

- **Total examples**: 8,432
- **Average test cases per problem**: ~10-15
- **Test case range**: 1-32 per problem
- **Dataset size**: ~10 GB uncompressed, ~10 GB compressed (includes test cases)
- **Format**: Parquet (11 shards, ~1GB each)
- **Schema**: VERL-compatible

## Data Quality

All problems in this dataset have been validated to ensure:

1. **Valid test cases**: Each problem has at least one valid test case
2. **Correct input/output pairs**: Test cases verified through sandbox execution
3. **Size constraints**: Test cases are within reasonable size limits (≤10MB)
4. **Format consistency**: All examples follow the same schema structure

## Conversion Script

The dataset was created using `preprocess_codecontests_verl.py`:

```bash
# Standard conversion (used for this dataset)
python preprocess_codecontests_verl.py \
  --dataset-id ByteDance-Seed/Code-Contests-Plus \
  --config 1x \
  --output-dir ./codecontests_verl_full \
  --sandbox-url http://localhost:8080/run_code \
  --batch-size 100

# Process with different configuration
python preprocess_codecontests_verl.py \
  --dataset-id ByteDance-Seed/Code-Contests-Plus \
  --config 2x \
  --output-dir ./codecontests_verl_2x \
  --sandbox-url http://localhost:8080/run_code \
  --batch-size 100

# Process limited samples for testing
python preprocess_codecontests_verl.py \
  --dataset-id ByteDance-Seed/Code-Contests-Plus \
  --config 1x \
  --output-dir ./codecontests_test \
  --sandbox-url http://localhost:8080/run_code \
  --max-examples 100
```

## Related Datasets

- [Code Contests Plus (Original)](https://huggingface.co/datasets/ByteDance-Seed/Code-Contests-Plus): Original dataset with competitive programming problems
- [Skywork-OR1-Code-VERL](https://huggingface.co/datasets/sungyub/skywork-or1-code-verl): Similar VERL-format dataset with 14,057 coding problems

## Additional Information

For more information about VERL format and usage in reinforcement learning, see:
- [VERL Documentation](https://verl.readthedocs.io/en/latest/preparation/prepare_data.html)
- [VERL GitHub Repository](https://github.com/volcengine/verl)

## Citation

If you use this dataset, please cite the original Code-Contests-Plus dataset:

```bibtex
@misc{code-contests-plus,
  title={Code-Contests-Plus},
  author={ByteDance-Seed},
  year={2024},
  publisher={HuggingFace},
  url={https://huggingface.co/datasets/ByteDance-Seed/Code-Contests-Plus}
}
```

## License

This dataset is released under the MIT License, following the license of the original Code-Contests-Plus dataset.