Datasets:

sungyub
/

eurus-2-code-verl

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: 3218296953
      num_examples: 25276
  download_size: 1652135331
  dataset_size: 3218296953
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
license: mit
task_categories:
  - reinforcement-learning
  - text-generation
tags:
  - code
  - reasoning
  - rlhf
  - verl

Eurus-2-Code-RL (VERL Format)

This dataset contains 25,276 competitive programming problems from the Eurus-2-RL-Data dataset, filtered and converted to VERL format for reinforcement learning training workflows.

Source: PRIME-RL/Eurus-2-RL-Data

License: MIT

Note (Updated 2025-10-27): System prompts have been removed from all examples for better compatibility with other code datasets. The dataset now contains only user messages with the coding problems. See changelog for details.

Dataset Description

Eurus-2-Code-RL is a curated collection of competitive programming problems specifically designed for training language models using reinforcement learning. The problems are sourced from various high-quality coding challenge platforms and include:

CodeContests problems
TACO (Text-Assisted Coding with Objectives) problems
APPS (Automated Programming Progress Standard) problems
Codeforces problems

Dataset Structure

The dataset follows the VERL format with the following fields:

data_source (string): Original source identifier (e.g., "taco", "codecontests", "apps", "codeforces")
prompt (list): Chat template format with role/content structure
- User message with the coding problem
ability (string): Task category ("code")
reward_model (dict): Evaluation information
- style: Evaluation method ("rule" for test-based evaluation)
- ground_truth: Test cases for evaluation
extra_info (dict): Additional metadata
- split: Data split ("train" or "dummy")
- index: Example index

Data Quality

High-Quality Problems:

✅ Diverse sources - Problems from competitive programming platforms
✅ RL-focused - Specifically designed for reinforcement learning training
✅ Verified solutions - Ground truth test cases for reward model evaluation
✅ Compatible format - Matches structure of other VERL code datasets

Sample Problem

{
  "data_source": "taco",
  "prompt": [
    {
      "role": "user",
      "content": "One tradition of ACM-ICPC contests is that a team gets a balloon for every solved problem. We assume that the submission time doesn't matter and teams are sorted only by the number of balloons they have. It means that one's place is equal to the number of teams with more balloons, increased by 1...\n\nWrite Python code to solve the problem. Present the code in \n```python\nYour code\n```\nat the end."
    }
  ],
  "ability": "code",
  "reward_model": {
    "style": "rule",
    "ground_truth": "{\"inputs\": [...], \"outputs\": [...]}"
  },
  "extra_info": {
    "split": "train",
    "index": 0
  }
}

Usage

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("sungyub/eurus-2-code-verl")

# Access an example
example = dataset['train'][0]
print(example['prompt'][0]['content'])  # Coding problem
print(example['reward_model']['ground_truth'])  # Test cases
print(example['data_source'])  # Source dataset

# Stream the dataset for memory efficiency
dataset = load_dataset("sungyub/eurus-2-code-verl", streaming=True)
for example in dataset['train']:
    # Process examples one at a time
    pass

Statistics

Total examples: 25,276
Format: 1 Parquet file with Git LFS
File size: ~1.54 GB
File: train-00000-of-00001.parquet
Filter rate: 5.3% of total Eurus-2 dataset

Source Datasets

The problems are sourced from multiple high-quality competitive programming datasets:

codecontests: CodeContests problems (9,639 problems)
taco: Text-Assisted Coding with Objectives (9,579 problems)
apps: Automated Programming Progress Standard (3,462 problems)
codeforces: Codeforces problems (2,596 problems)

Problem Types

The dataset covers a wide range of programming challenges including:

Algorithm design and implementation
Data structures
Dynamic programming
Graph algorithms
String processing
Mathematical problems
And more...

File Structure

The dataset is contained in a single parquet file:

File name: train-00000-of-00001.parquet
Contains all 25,276 examples
HuggingFace datasets library automatically handles file loading

Conversion

The dataset was converted using a streaming approach:

# Install dependencies
pip install datasets pyarrow

# Run conversion
python convert_to_verl.py

# Features:
# - Streaming processing for memory efficiency
# - ParquetWriter for efficient output
# - Progress tracking and resume capability
# - Filters only code problems (ability='code')

Use Cases

This dataset is ideal for:

Reinforcement Learning: Training code generation models with RL
Fine-tuning: Improving competitive programming capabilities
Code Generation: Training models to solve algorithmic problems
Dataset Merging: Compatible with other VERL code datasets (e.g., skywork-or1-code-verl)

Technical Details

Conversion Process

Loaded source dataset from HuggingFace in streaming mode
Filtered examples where ability='code'
Removed system prompts for compatibility (2025-10-27)
Output to single parquet file
Total conversion time: ~2.4 minutes
Filter rate: 5.3% (25,276 code problems from 480,537 total)

VERL Format Benefits

Standardized structure: Consistent across all VERL datasets
Rich metadata: Includes source and split information
Chat template: Ready for instruction-tuned models
Reward model integration: Test cases for RL training
Dataset compatibility: Works seamlessly with other VERL code datasets

Original System Prompt (Removed)

The original dataset included a structured reasoning system prompt with the following actions:

[ASSESS]: Evaluate the current state
[ADVANCE]: Take a concrete step forward
[VERIFY]: Check validity of steps
[SIMPLIFY]: Break down complex parts
[SYNTHESIZE]: Combine insights
[PIVOT]: Change approach if needed
[OUTPUT]: Present the final answer

This prompt has been removed from all examples to ensure compatibility with other code datasets that use simple user-only prompts. Users who wish to use structured reasoning can add their own system prompts at training time.

Additional Information

For more information about VERL format, see the VERL documentation.

Citation

If you use this dataset, please cite the original Eurus-2-RL-Data:

@misc{eurus-2-rl-data,
  title={Eurus-2-RL-Data},
  author={PRIME-RL},
  year={2024},
  publisher={HuggingFace},
  url={https://huggingface.co/datasets/PRIME-RL/Eurus-2-RL-Data}
}

Changelog

2025-10-27 - System Prompt Removal

Removed system prompts from all 25,276 examples
Improved compatibility with other VERL code datasets (e.g., skywork-or1-code-verl)
Prompt structure now: [{"role": "user", "content": "..."}] instead of [{"role": "system", ...}, {"role": "user", ...}]
All other fields (data_source, ability, reward_model, extra_info) preserved
Original system prompt content documented above for reference
File size remains ~1.54GB

2025-10-14 - Initial Release

Filtered and converted 25,276 code problems from Eurus-2-RL-Data
Single file for efficient loading
Preserved original source information and metadata
Included structured reasoning system prompt
Total size: 1.54GB