| --- |
| license: mit |
| task_categories: |
| - other |
| tags: |
| - smoldataenvs |
| - rl-environment |
| - agent |
| - data-analysis |
| - reinforcement-learning |
| - code-agent |
| - harbor |
| - openenv |
| --- |
| |
| <div align="center"> |
|
|
| <img src="https://huggingface.co/datasets/FineEnvs/SmolDataEnvs-harbor-train/resolve/main/banner.png" alt="SmolDataEnvs" width="100%"> |
|
|
| # 📊 SmolDataEnvs — Harbor (train) |
|
|
| [](https://huggingface.co/collections/FineEnvs/smoldataenvs) |
| [](https://huggingface.co/spaces/HuggingFaceH4/harbor-visualiser?dataset=FineEnvs/SmolDataEnvs-harbor-train) |
|
|
| </div> |
|
|
| > **5.5K+ RL tasks for hill-climbing small models in code and data science.** |
|
|
| <div align="center"> |
|
|
| <img src="https://huggingface.co/datasets/FineEnvs/SmolDataEnvs-harbor-train/resolve/main/curves.gif" alt="Reward and held-out pass@k climbing over 1,119 GRPO steps" width="100%"> |
|
|
| <sub>A 2B model on these tasks: reward, tool efficiency and held-out pass@k over 1,119 GRPO steps.</sub> |
|
|
| </div> |
|
|
|
|
| The training suite: **5,000 hands-on data-analysis tasks**. Each one drops an agent into a sandbox with a |
| real dataset and a question, and asks it to explore the data, compute the answer, and write it down. |
| Every answer is checked deterministically. |
|
|
|
|
| Packaged in [Harbor](https://github.com/huggingface/OpenEnv) format, so each task is a ready-made |
| agentic environment: its own container, its own data, its own verifier. |
|
|
| ## What's inside |
|
|
| - **5,000 verified tasks** — the RL training set |
| - **Difficulty** — easy **1,433** · medium **2,845** · hard **722** (`difficulty_tier`, plus `difficulty_level` 1–5) |
|
|
| ## How a task is laid out |
|
|
| ``` |
| tasks/<task_id>/ |
| task.toml # metadata, the question, the gold answer, grading tolerances |
| instruction.md # the prompt the agent sees |
| environment/ # Dockerfile (shared base image) + the data-pull hook |
| tests/ # grader.py (deterministic) + test.sh |
| registry.json # the suite manifest |
| manifest.parquet # one row per task, for filtering without walking the tree |
| ``` |
|
|
| ## Serve it |
|
|
| ```bash |
| openenv harbor serve \ |
| --dataset FineEnvs/SmolDataEnvs-harbor-train \ |
| --llm-url http://127.0.0.1:8000/v1 --model <your-model> \ |
| --port 8000 --capture-port 8100 |
| ``` |
|
|
| Pass several with `--dataset a,b` and each arrives as its own split, which is how you train against |
| `-train` and validate against `-eval` from one server. |
|
|
| ## One rollout, no trainer |
|
|
| ```bash |
| openenv harbor rollout \ |
| --dataset FineEnvs/SmolDataEnvs-harbor-train \ |
| --llm-url http://127.0.0.1:8000/v1 --model <your-model> \ |
| --harness opencode --sandbox e2b --task-index 0 |
| ``` |
|
|
| ## Where it comes from |
|
|
| Built from the [jupyter-agent dataset](https://huggingface.co/datasets/jupyter-agent/jupyter-agent-dataset) |
| — real data-science notebooks over 471 Kaggle datasets. Every question–answer pair was extracted and |
| then **verified**: strong agent models had to solve the task in a live sandbox and reproduce the gold |
| answer under deterministic grading. Anything ambiguous or un-checkable was dropped. So every task |
| here is known-solvable and unambiguously gradable. |
|
|
| **Verified by a checker, not judged by a model.** Grading is an exact comparison against a known |
| answer, through a ladder of checks: exact match → numeric with tolerances → list and percent |
| normalisation → symbolic equivalence. No LLM sits in the reward path, so the signal does not drift |
| when you change the grader's model, because there isn't one. |
|
|
| ## The family |
|
|
| | Repo | What it is | |
| |---|---| |
| | [`SmolDataEnvs`](https://huggingface.co/datasets/FineEnvs/SmolDataEnvs) | the tasks as plain rows — load it and prompt any model | |
| | [`SmolDataEnvs-sft`](https://huggingface.co/datasets/FineEnvs/SmolDataEnvs-sft) | 4,677 verified agent trajectories, TRL-ready | |
| | [`SmolDataEnvs-harbor-train`](https://huggingface.co/datasets/FineEnvs/SmolDataEnvs-harbor-train) | 5,000 tasks as Harbor environments | |
| | [`SmolDataEnvs-harbor-test`](https://huggingface.co/datasets/FineEnvs/SmolDataEnvs-harbor-test) | 250 held-out, deliberately harder | |
| | [`SmolDataEnvs-harbor-eval`](https://huggingface.co/datasets/FineEnvs/SmolDataEnvs-harbor-eval) | 144 for quick validation during a run | |
|
|
