README.md

metadata

license: cc-by-4.0
task_categories:
  - reinforcement-learning
language:
  - en
tags:
  - harbor
  - nemotron-gym
  - rl
  - verifiable-rewards
size_categories:
  - 10K<n<100K

laion/nemotron-gym-competitive-coding

Harbor-format conversion of nvidia/Nemotron-RL-coding-competitive_coding.

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

stdio_diff (run /app/solution.py against hidden stdin/stdout test cases) — see the source converter for full details.

Usage with Harbor

from datasets import load_dataset

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

Source

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