README.md

---
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)

**5.5K+ RL tasks for hill-climbing small models in code and data science.**

[![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)
[![Harbor Visualiser](https://img.shields.io/badge/%F0%9F%A4%97%20Harbor%20Visualiser-Browse%20tasks-FFD21E?style=for-the-badge&labelColor=1a1a1a)](https://huggingface.co/spaces/HuggingFaceH4/harbor-visualiser?dataset=FineEnvs/SmolDataEnvs-harbor-train)

</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 |