| --- |
| license: mit |
| task_categories: |
| - text-generation |
| tags: |
| - smoldataenvs |
| - sft |
| - agent |
| - tool-calling |
| - data-analysis |
| - trl |
| --- |
| |
| <div align="center"> |
|
|
| <img src="https://huggingface.co/datasets/FineEnvs/SmolDataEnvs-sft/resolve/main/banner.png" alt="SmolDataEnvs" width="100%"> |
|
|
| # 🛠️ SmolDataEnvs: SFT |
|
|
| [](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-sft/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> |
|
|
|
|
| **4,677 worked examples** of an agent doing data science the right way. Each row is a complete, |
| verified-correct trajectory: read the question, poke at the data with a shell tool, reason, compute, |
| write the answer. Every one of them solved its task and passed the deterministic grader, so you are |
| fine-tuning on demonstrations that are known to be correct rather than merely plausible. |
|
|
| Drop-in ready for [TRL](https://github.com/huggingface/trl): conversational `messages` plus `tools`. |
|
|
|
|
| ## What's inside |
|
|
| - **4,677 correct trajectories**, one per task |
| - **Difficulty**: easy 1,402 · medium 2,640 · hard 635 |
| - One tool throughout: `bash` |
|
|
| ## What's in a row |
|
|
| - **`messages`**: the full conversation in OpenAI/TRL chat format: `system` → `user` (the task) → |
| `assistant` (reasoning plus `tool_calls`) → `tool` (command output) → … → final `assistant` answer |
| - **`tools`**: the `bash` tool's JSON schema, for `apply_chat_template(..., tools=...)` |
| - `task_id`, `difficulty` (1–5), `difficulty_tier`, `n_turns`, `source_agent` |
|
|
| ## Fine-tune with TRL |
|
|
| ```python |
| from datasets import load_dataset |
| from trl import SFTConfig, SFTTrainer |
| |
| ds = load_dataset("FineEnvs/SmolDataEnvs-sft", split="train") |
| trainer = SFTTrainer( |
| model="HuggingFaceTB/SmolLM3-3B", |
| train_dataset=ds, |
| args=SFTConfig(output_dir="smoldataenvs-sft", max_length=8192), |
| ) |
| trainer.train() |
| ``` |
|
|
| A runnable notebook and a single-file script for HF Jobs are in |
| [FineEnvs/04-smoldataenvs](https://github.com/adithya-s-k/FineEnvs/tree/main/04-smoldataenvs). |
|
|
| ## 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 | |
|
|
| ## 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} |
| } |
| ``` |
|
|
