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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
```
## 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.
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