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End of preview. Expand in Data Studio
laion/nemotron-gym-knowledge-openqa
Harbor-format conversion of nvidia/Nemotron-RL-knowledge-openqa.
Each row contains:
| column | type | description |
|---|---|---|
path |
string | Deterministic short ID (<family>-<sha256[:12]>.tar.gz) |
task_binary |
binary | Gzipped tar containing the full Harbor task |
The tarball contents follow Harbor's task layout:
instruction.md # Prompt shown to the agent
environment/Dockerfile # python:3.11-slim-bookworm base + task-specific pip deps
tests/test.sh # Verifier entrypoint (writes /logs/verifier/reward.txt)
tests/verifier.py # Verifier implementation (embedded, deterministic)
tests/verifier_data.json # Per-task verifier inputs (JSON, no code interpolation)
metadata.json # Provenance: source_dataset, row_index, family, ...
task.toml # Standard Harbor task config (cpu/memory/timeout defaults)
Conversion details
Generated by the data/nemotron_gym adapter in
OpenThoughts-Agent.
Conversion is secure-by-construction:
- Dataset content is never interpolated into shell, Python, or Dockerfile source.
All values flow through
tests/verifier_data.json(JSON, parsed at runtime). - Base image is name-pinned (
python:3.11-slim-bookworm); pip specs validated against a strict allowlist regex. - Text fields stripped of C0/C1 control characters; lengths capped; tarball paths validated against traversal / NUL / absolute-path attacks.
- Tarballs are deterministic (sorted entries,
mtime=0,uid/gid=0) → reproducible bytes.
Verifier family
normalized_text (normalize whitespace/case/punct, substring match) — see the source converter for full details.
Usage with Harbor
from datasets import load_dataset
ds = load_dataset("laion/nemotron-gym-knowledge-openqa", split="train")
print(ds[0]["path"], len(ds[0]["task_binary"]))
To run a single task with Harbor:
# Extract one task to a directory and point Harbor at it
python - <<'PY'
import gzip, io, tarfile
from datasets import load_dataset
ds = load_dataset("laion/nemotron-gym-knowledge-openqa", split="train")
row = ds[0]
with tarfile.open(fileobj=io.BytesIO(row["task_binary"]), mode="r:gz") as tar:
tar.extractall("/tmp/knowledge-openqa-task")
PY
harbor run -t /tmp/knowledge-openqa-task -e daytona # or -e docker
Source
This dataset is a derivative of nvidia/Nemotron-RL-knowledge-openqa, part of NVIDIA's NeMo-Gym collection.
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