README.md

---
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

[![Collection](https://img.shields.io/badge/%F0%9F%A4%97%20Collection-SmolDataEnvs-FFD21E?style=for-the-badge&labelColor=1a1a1a)](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}
}
```