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D3-Gym is the first automatically constructed dataset of verifiable environments for Data-Driven Discovery. It contains 565 tasks derived from 239 real-world multi-disciplinary scientific repositories.

Each task includes:

• a natural language instruction,
• an executable environment with pre-installed dependencies,
• input datasets and artifact previews,
• a reference implementation,
• and an automatically generated evaluation script.

All task environments are distributed as Docker images via Docker Hub.

Each image is a self-contained unit representing a single data-driven discovery task. It includes the task specification, datasets and previews, reference outputs, and evaluation script, along with pre-installed dependencies.

To solve a task, provide a solution.py that:

• reads the provided datasets, and
• writes outputs to pred_results/.

The evaluation script compares your outputs against the reference and returns a pass/fail decision with a short explanation.

For easier browsing, we also provide an annotation sheet with metadata for all tasks on HuggingFace.

Pull a task image and inspect it:

docker pull hananemoussa/d3-gym:task_1
docker run --rm hananemoussa/d3-gym:task_1 inspect

Run your solution and evaluate:

docker run --rm \
  -v $(pwd)/solution.py:/task/solution.py:ro \
  hananemoussa/d3-gym:task_1 run_and_eval

Each Docker image exposes the following directory layout:

/task/
  task_instruction.txt     # task description
  datasets/                # input data (CSV, JSON, images, etc.)
  *_preview.txt            # dataset schema previews
  eval_script.py           # evaluation logic
  gold_results/            # reference outputs
  pred_results/            # expected location for your outputs
  entrypoint.sh            # command routing

# Run and evaluate a solution
docker run --rm \
  -v $(pwd)/solution.py:/task/solution.py:ro \
  hananemoussa/d3-gym:task_151 run_and_eval

# Evaluate precomputed results
docker run --rm \
  -v $(pwd)/my_results:/task/pred_results:ro \
  hananemoussa/d3-gym:task_151 eval

# Interactive debugging session
docker run --rm -it hananemoussa/d3-gym:task_151 shell

D3-Gym supports workflows that require executable environments with verifiable evaluation signals for data-driven discovery (e.g. reinforcement learning, self-improvement, etc.).

One use case is generating training trajectories (e.g., reasoning traces and solutions). The trajectories used in our experiments are available on HuggingFace.

Repositories used in the creation of D3-Gym are under permissive licenses. We provide a full breakdown of licenses below. There are also 39 repositories that do not provide any license information; we assume these permit use for research purposes.

• BrainIAC
• DeepDelta

If you find our paper or resources useful in your work, please cite us:

@article{d3gym2026,
  title   = {D3-Gym: Constructing Verifiable Environments for Data-Driven Discovery},
  author  = {Hanane Nour Moussa, Yifei Li, Zhuoyang Li, Yankai Yang, Cheng Tang, Tianshu Zhang, Nesreen K. Ahmed, Ali Payani, Ziru Chen, Huan Sun},
  journal = {arXiv preprint arXiv:2604.27977},
  year    = {2026},
  url     = {https://arxiv.org/abs/2604.27977}
}