Datasets:

laion
/

nemotron-gym-reasoning-gym-v2

path stringlengths 33 33	task_binary unknown
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reasoning-gym-98ed825fbe02.tar.gz	[ 31, 139, 8, 0, 248, 231, 10, 106, 0, 255, 237, 26, 107, 115, 219, 54, 242, 62, 243, 87, 108, 153, 153, 86, 186, 138, 180, 30, 126, 84, 234, 40, 51, 73, 236, 180, 158, 187, 58, 25, 215, 185, 76, 38, 201, 208, 20, 9, 137, ...
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laion/nemotron-gym-reasoning-gym-v2

Harbor-format conversion of nvidia/Nemotron-RL-ReasoningGym-v1.

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

reasoning_gym (delegate to upstream reasoning_gym scorer; normalized-match fallback) — see the source converter for full details.

Usage with Harbor

from datasets import load_dataset

ds = load_dataset("laion/nemotron-gym-reasoning-gym-v2", 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-reasoning-gym-v2", split="train")
row = ds[0]
with tarfile.open(fileobj=io.BytesIO(row["task_binary"]), mode="r:gz") as tar:
    tar.extractall("/tmp/reasoning-gym-task")
PY
harbor run -t /tmp/reasoning-gym-task -e daytona  # or -e docker

Source

This dataset is a derivative of nvidia/Nemotron-RL-ReasoningGym-v1, part of NVIDIA's NeMo-Gym collection.

v2 changelog

Fixed sandbox build failure: the Dockerfile now installs build-essential + python3-dev so pip can compile pycosat (C extension, sdist-only on PyPI) and cellpylib (sdist-only) during pip install reasoning-gym==0.1.20. v1 had 0% infra success because the slim-bookworm base image lacked a C toolchain.
Fixed verifier API mismatch: the embedded verifier now calls reasoning_gym.get_score_answer_fn(source) (the actual public API as of v0.1.19+) instead of the non-existent get_scorer. The normalized-exact-match fallback is preserved.

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