---
license: mit
task_categories:
- text-generation
language:
- en
tags:
- code
- reasoning
- chain-of-thought
- verifiable
- execution-verified
- python
- sft
- synthetic
pretty_name: Verifiable Code Reasoning
size_categories:
- 1M<n<10M
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
dataset_info:
  features:
  - name: id
    dtype: string
  - name: difficulty
    dtype: string
  - name: category
    dtype: string
  - name: problem
    dtype: string
  - name: signature
    dtype: string
  - name: reasoning
    dtype: string
  - name: code
    dtype: string
  - name: tests
    dtype: string
  - name: verified
    dtype: bool
  - name: language
    dtype: string
  - name: code_hash
    dtype: string
  - name: sft_text
    dtype: string
  - name: meta
    dtype: string
  splits:
  - name: train
    num_bytes: 1622807453
    num_examples: 1470000
  - name: validation
    num_bytes: 33093634
    num_examples: 30000
  download_size: 171472600
  dataset_size: 1655901087
---

<div align="center">

# Verifiable Code Reasoning

### Execution-verified Python problems with chain-of-thought

**Sandbox-checked solutions · Multi-test unit checks · Deduplicated instances · Training-ready `sft_text`**

[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
[![Examples](https://img.shields.io/badge/examples-1,500,000-blue)](https://huggingface.co/datasets/smshahbaj/verifiable-code-reasoning)

</div>

---

## Overview

**Verifiable Code Reasoning** is a large-scale dataset of Python coding problems where every kept solution has **passed sandboxed unit tests**.

Unlike scraped contest dumps or unverified LLM traces, an example enters this release only if:

1. a reference implementation exists,
2. it runs under a time-limited sandbox, and
3. it matches expected outputs on **at least 3** tests.

Failed executions are discarded. Near-duplicate *instances* are filtered by content fingerprint (signature + tests), so shared solution templates across different inputs are allowed, but repeated test instances are not.

| Property | Detail |
|----------|--------|
| **Verification** | Multiprocess sandbox, timeout, ≥3 tests |
| **Dedup** | Unique `id` + instance content hash (signature + tests) |
| **Balance** | Category & difficulty quotas during generation |
| **Resume** | Hub checkpoints: `train.jsonl`, `progress.json` |
| **License** | MIT |

**Current release size:** **1,500,000** verified examples (generation target 1,500,000).

---

## Dataset statistics

### Difficulty

| Difficulty | Count |
|------------|------:|
| easy | 783,088 |
| medium | 698,789 |
| hard | 18,123 |

### Category

| Category | Count |
|----------|------:|
| array | 313,191 |
| hashmap | 240,984 |
| two_pointers | 218,129 |
| greedy | 144,377 |
| binary_search | 134,400 |
| dp | 134,400 |
| string | 133,673 |
| bit | 132,948 |
| math | 46,941 |
| stack | 702 |
| matrix | 255 |

---

## Schema

| Field | Type | Description |
|-------|------|-------------|
| `id` | string | Stable example id |
| `problem` | string | Natural-language problem statement |
| `signature` | string | Python function signature |
| `reasoning` | string | Numbered chain-of-thought |
| `code` | string | Reference Python solution |
| `tests` | string | JSON list of `{"input": ..., "output": ...}` (≥3) |
| `category` | string | array, hashmap, dp, binary_search, ... |
| `difficulty` | string | easy / medium / hard |
| `sft_text` | string | Ready-to-train prompt block |
| `verified` | bool | Always `true` in this release |
| `code_hash` | string | Hash of solution source (audit) |
| `language` | string | `python` |

> `tests` is stored as a **JSON string** so Arrow/HF can keep one schema while outputs may be int, bool, or list.

---

## Quick start

```python
from datasets import load_dataset
import json

ds = load_dataset("smshahbaj/verifiable-code-reasoning")
row = ds["train"][0]
print(row["sft_text"][:500])
tests = json.loads(row["tests"])
print(tests[0])
```

### SFT format (`sft_text`)

```text
### Problem
...

### Reasoning
1) ...
2) ...

### Solution
```python
...
```
```

---

## How it was built

1. **25** algorithmic generator families (arrays, strings, math, DP, greedy, binary search, bits, matrices, …)
2. Each example gets **≥3** unit tests (including varied inputs)
3. Solution is executed in a **sandbox**; mismatch / timeout / exception → drop
4. **Quotas** limit over-representation of a single category or difficulty
5. **Instance dedup** on signature+tests fingerprint (unrepeatable test cases)
6. Multi-session scale via Hugging Face checkpoints every 2000 new verifies

---

## Intended uses

- Supervised fine-tuning on **code + reasoning**
- Process / outcome supervision signals (execution as ground truth)
- Filtering or ranking other synthetic coding traces

## Out of scope / limitations

- Coverage is bounded by the **generator bank** (not full competitive-programming breadth)
- **Python only** in this release
- Synthetic style can still be distributionally narrow — always evaluate models on real benchmarks (HumanEval, MBPP, LiveCodeBench, etc.)
- Not a substitute for human-written contest editorials

---

## Citation

```bibtex
@misc{verifiable_code_reasoning,
  title        = {Verifiable Code Reasoning},
  author       = {smshahbaj},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/datasets/smshahbaj/verifiable-code-reasoning}},
  note         = {Execution-verified Python coding CoT dataset}
}
```

---

<div align="center">

**Verified or it does not ship.**

</div>