README.md

---
license: other
task_categories:
- text-generation
- reinforcement-learning
language:
- en
tags:
- code-generation
- reinforcement-learning
- agentic-rl
- polar
- slime
- harnessmix
pretty_name: HarnessMix Codegen RL Data
size_categories:
- 1K<n<10K
---

# HarnessMix Codegen RL Data

This repository contains the current HarnessMix no-Docker code-generation training and held-out evaluation data prepared from a local Harness-RL workspace.

The rows are Slime/Polar JSONL examples. Each row has a chat-style `prompt`, an empty `label`, and `metadata` for benchmark provenance, source/test assets, evaluator command, and contamination controls.

## Files

- `data/train.jsonl`: 3992 training rows.
- `data/train_manifest.jsonl`: audit manifest for the training rows.
- `data/eval.jsonl`: 559 held-out evaluation rows, combining EvalPlus, OJBench, and the current LiveCodeBench smoke row.
- `data/eval_manifest.jsonl`: audit manifest for all evaluation rows.
- `data/eval_evalplus_ojbench.jsonl`: 558 EvalPlus + OJBench held-out rows.
- `data/eval_livecodebench_smoke.jsonl`: 1 LiveCodeBench smoke row.
- `assets/`: source and test files referenced by the JSONL metadata.
- `raw/ojbench_testdata/`: selected OJBench raw prompt/testdata files needed by the current OJBench subset.
- `metadata/`: generation and filtering reports.

## Training Split

All training rows have `metadata.split = "train"`, `metadata.scenario = "code_generation"`, and `metadata.contamination.heldout = false`.

| Benchmark | Rows | Upstream/source |
|---|---:|---|
| DeepCoder PrimeIntellect | 1150 | `agentica-org/DeepCoder-Preview-Dataset:primeintellect:train` |
| LeetCodeDataset train | 979 | `newfacade/LeetCodeDataset:train` |
| CodeContests Plus | 912 | `ByteDance-Seed/Code-Contests-Plus:1x:train` |
| CodeContests-O | 660 | `OctoReasoner/Code-Contests-O:train` |
| AutoCodeBenchmark Python | 177 | `tencent/AutoCodeBenchmark:autocodebench:train:python_only` |
| MBPP sanitized train | 114 | `google-research-datasets/mbpp:sanitized:train` |
| **Total** | **3992** |  |

This training mix was produced by the HarnessMix quality/diversity filter. It excludes EvalPlus, LiveCodeBench, OJBench, raw APPS/TACO/deepmind CodeContests, and other explicitly held-out evaluation sources.

## Held-Out Evaluation Split

All evaluation rows have `metadata.split = "eval"`, `metadata.contamination.heldout = true`, and `metadata.contamination.exclude_from_training = true`.

| Benchmark | Rows | Upstream/source | Notes |
|---|---:|---|---|
| EvalPlus HumanEval+ | 164 | `evalplus/humanevalplus` | Functional Python code-generation eval. |
| EvalPlus MBPP+ | 378 | `evalplus/mbppplus` | Functional Python code-generation eval. |
| OJBench Python | 16 | `He-Ren/OJBench_testdata:prompts/full.jsonl` | Competition-style Python subset; requires OJBench/DMOJ runtime for execution. |
| LiveCodeBench code generation | 1 | `livecodebench/code_generation:test` | Smoke row only; larger streaming runs were blocked by the large official JSONL and slow remote reads. |
| **Total** | **559** |  |  |

## Schema

Each JSONL row has this shape:

```json
{
  "prompt": [{"role": "user", "content": "..."}],
  "label": "",
  "metadata": {
    "benchmark": "...",
    "split": "train|eval",
    "scenario": "code_generation",
    "example_id": "...",
    "dataset_id": "...",
    "source_path": "assets/.../solution.py",
    "test_path": "assets/.../test_solution.py",
    "prepare_actions": [{"type": "upload_file", "source": "assets/...", "target": "/polar/session/workspace/..."}],
    "evaluator_command": "python3 test_solution.py",
    "expected_output_json": {"...": "PASSED"},
    "contamination": {
      "heldout": false,
      "exclude_from_training": false,
      "do_not_mix_with_eval_benchmarks": ["EvalPlus", "LiveCodeBench", "OJBench"]
    }
  }
}
```

Paths in this repository are relative to the dataset repository root. If you run the rows from a different working directory, either run from the downloaded dataset root or rewrite `metadata.source_path`, `metadata.test_path`, and `metadata.prepare_actions[].source` accordingly.

## Evaluation Runtime Notes

EvalPlus and LiveCodeBench rows use local Python test files under `assets/` and `python3 test_solution.py` as the evaluator command.

OJBench rows call the official `ojbench`/DMOJ judge from their generated `test_solution.py`. To execute OJBench rows, install the OJBench package and its runtime dependencies (`DMOJ`, `pypy3`, and a C++17 compiler such as `g++`). The selected testdata is included under `raw/ojbench_testdata/`.

## Training-Time Eval Example

In Harness-RL, the no-Docker launcher can pass held-out data to Slime eval with name/path pairs:

```bash
PROMPT_DATA=data/train.jsonl PROMPT_MANIFEST=data/train_manifest.jsonl EVAL_PROMPT_DATA="heldout data/eval.jsonl" EVAL_INTERVAL=5 N_SAMPLES_PER_EVAL_PROMPT=1 EVAL_MAX_RESPONSE_LEN=4096 bash examples/harnessmix/no_docker_smoke/run_train.sh
```

## ProRL-Agent-Server / Polar Slime Usage

The ProRL `codegen` branch should track code, configs, and documentation only. It should not duplicate this dataset in Git. From a ProRL-Agent-Server checkout, download the dataset into the codegen example directory before running smoke tests or GRPO training:

```bash
export HF_TOKEN=<your-huggingface-token>  # only needed if this dataset is private
python - <<'PY_INNER'
import os
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="Xnhyacinth/codegen",
    repo_type="dataset",
    local_dir="examples/codegen_slime_grpo",
    token=os.environ.get("HF_TOKEN"),
    allow_patterns=["data/**", "assets/**", "metadata/**", "raw/**"],
)
PY_INNER
```

After download, the ProRL codegen example expects these local paths:

- `examples/codegen_slime_grpo/data/train.jsonl`
- `examples/codegen_slime_grpo/data/eval.jsonl`
- `examples/codegen_slime_grpo/assets/`
- `examples/codegen_slime_grpo/metadata/`
- `examples/codegen_slime_grpo/raw/ojbench_testdata/` for the current OJBench subset

Minimal ProRL smoke command:

```bash
RUN_ID=codegen-smoke PROMPT_DATA=examples/codegen_slime_grpo/data/train_smoke.jsonl REF_LOAD=/path/to/Qwen3.5-4B_torch_dist WANDB_MODE=offline CUDA_VISIBLE_DEVICES=0,1,2,3 bash examples/codegen_slime_grpo/run.sh
```

For full training, use `PROMPT_DATA=data/train.jsonl` and `EVAL_PROMPT_DATA=data/eval.jsonl` through the ProRL codegen launcher. Keep W&B logs, checkpoints, Ray temp files, and downloaded dataset files outside Git.

## Provenance and Licensing

This dataset is a processed mix of multiple upstream public datasets and benchmarks. Users are responsible for complying with each upstream dataset's license and usage terms. This repository does not claim a single unified license over upstream content.

The included data is intended for code-generation RL training/evaluation research and contamination-controlled held-out measurement.