| --- |
| license: mit |
| task_categories: |
| - question-answering |
| - table-question-answering |
| tags: |
| - smoldataenvs |
| - data-analysis |
| - agent |
| - rl-environment |
| - reinforcement-learning |
| - code-agent |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| - split: test |
| path: data/test-* |
| - split: eval |
| path: data/eval-* |
| --- |
| |
| <div align="center"> |
|
|
| <img src="https://huggingface.co/datasets/FineEnvs/SmolDataEnvs/resolve/main/banner.png" alt="SmolDataEnvs" width="100%"> |
|
|
| # 📈 SmolDataEnvs |
|
|
| [](https://huggingface.co/collections/FineEnvs/smoldataenvs) |
|
|
| </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/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. Left: what it optimises. Right: 144 held-out tasks it never trains on.<br> |
| Two runs over the same 5,000 tasks: <b>shuffled</b> against a <b>curriculum</b> ordered easiest to hardest.</sub> |
|
|
| </div> |
|
|
|
|
| Data-analysis tasks as a plain, load-and-go dataset: **no runtime, no framework required**. Each |
| row is one self-contained task: a real tabular dataset, a question about it, and a gold answer a |
| bundled grader can check deterministically. Load it, prompt any model however you like, grade the |
| result. |
|
|
| This is the front door. If you want the tasks as runnable sandboxed environments, use the |
| [Harbor suites](https://huggingface.co/datasets/FineEnvs/SmolDataEnvs-harbor-train); if you want |
| demonstrations to fine-tune on, use [`-sft`](https://huggingface.co/datasets/FineEnvs/SmolDataEnvs-sft). |
|
|
|
|
| ## Splits |
|
|
| | Split | Tasks | Easy | Medium | Hard | What it's for | |
| |---|---|---|---|---|---| |
| | `train` | 5,000 | 1,433 | 2,845 | 722 | training | |
| | `test` | 250 | 33 | 118 | 99 | held-out benchmark, deliberately harder | |
| | `eval` | 144 | 16 | 74 | 54 | quick validation during a run | |
|
|
| The held-out splits are harder than train by construction: train is 29% easy and 14% hard, the |
| held-out splits are 11–13% easy and 38–40% hard. Worth knowing before you read any eval number. |
|
|
| ## What's in a row |
|
|
| | Column | Meaning | |
| |---|---| |
| | `task_id`, `source_row_id` | identifiers | |
| | `question` | the question to answer | |
| | `answer` | the gold answer | |
| | `reward_mode`, `atol`, `rtol` | how to grade it: match type and numeric tolerances | |
| | `difficulty_level` (1–5), `difficulty_tier` | difficulty | |
| | `kaggle_dataset` | the source dataset | |
| | `hf_bucket`, `bucket_prefix`, `files` | where the input files live and what they are | |
| | `instruction` | the full agent prompt | |
| | `package_tier` | environment sizing hint | |
|
|
| ## Load it |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("FineEnvs/SmolDataEnvs", split="test") |
| row = ds[0] |
| print(row["question"], "→", row["answer"], f"({row['reward_mode']})") |
| ``` |
|
|
| ## Grab the data files for a task |
|
|
| The tables live in a Hugging Face **bucket**, so they come down with the bucket API rather than |
| `snapshot_download`: |
|
|
| ```python |
| from huggingface_hub import list_bucket_tree, download_bucket_files |
| |
| prefix = row["bucket_prefix"].rstrip("/") + "/" |
| items = [i for i in list_bucket_tree(row["hf_bucket"], prefix=prefix, recursive=True) |
| if getattr(i, "type", None) == "file"] |
| download_bucket_files(row["hf_bucket"], |
| files=[(i.path, "input/" + i.path.split("/")[-1]) for i in items]) |
| ``` |
|
|
| ## Grade a prediction |
|
|
| `grader.py` ships in this repo. It scores an answer through a ladder of checks: exact → numeric |
| with `atol`/`rtol` → list and percent normalisation → symbolic equivalence: |
|
|
| ```python |
| from huggingface_hub import hf_hub_download |
| import importlib.util, sys |
| |
| path = hf_hub_download("FineEnvs/SmolDataEnvs", "grader.py", repo_type="dataset") |
| spec = importlib.util.spec_from_file_location("grader", path) |
| grader = importlib.util.module_from_spec(spec) |
| sys.modules["grader"] = grader # the dataclasses inside it need this |
| spec.loader.exec_module(grader) |
| |
| r = grader.grade(row["answer"], my_prediction, reward_mode=row["reward_mode"], |
| abs_tol=row["atol"], rel_tol=row["rtol"]) |
| print(r.reward, r.method) # 1.0 exact | 0.0 miss |
| ``` |
|
|
| ## 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 | |
|
|
| ## Train on it |
|
|
| The simplest path, a notebook and a single-file script you can hand to HF Jobs, lives in |
| [FineEnvs/04-smoldataenvs](https://github.com/adithya-s-k/FineEnvs/tree/main/04-smoldataenvs). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{fineenvs, |
| author = {Kolavi, Adithya S}, |
| title = {FineEnvs: Open Source RL Environments for LLM Agents}, |
| year = {2026}, |
| url = {https://github.com/adithya-s-k/FineEnvs} |
| } |
| ``` |
|
|
