Blame for 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-test/resolve/main/banner.png" alt="SmolDataEnvs" width="100%">

# 📊 SmolDataEnvs: Harbor (test)

[![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-test)

</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-test/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>


The held-out benchmark: **250 tasks** the model never trains on, weighted towards the hard end on
purpose. This is the suite to quote a number from.


Packaged in [Harbor](https://github.com/openenv/OpenEnv) format, so each task is a ready-made
agentic environment: its own container, its own data, its own verifier.

## What's inside

- **250 verified tasks**: held out, deliberately harder than train
- **Difficulty**: easy **33** · medium **118** · hard **99** (`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
```

## Install

```bash
pip install "openenv[harbor]"   # tested with openenv 0.6.0, needs Python 3.12+
```

The `[harbor]` extra is what brings in the sandboxes. A plain `pip install openenv` gives you
the CLI but nothing to run a task in. `--sandbox e2b` also needs `E2B_API_KEY` set.

## Serve it

```bash
openenv harbor serve \
  --dataset FineEnvs/SmolDataEnvs-harbor-test \
  --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-test \
  --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 |

## 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}
}
```