Datasets:

smshahbaj
/

verifiable-code-reasoning

license: mit
task_categories:
  - text-generation
  - text2text-generation
language:
  - en
tags:
  - code
  - reasoning
  - chain-of-thought
  - verifiable
  - execution-verified
  - python
  - sft
pretty_name: Verifiable Code Reasoning
size_categories:
  - 10K<n<100K
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: 25363
      num_examples: 24
    - name: validation
      num_bytes: 1318
      num_examples: 1
  download_size: 34096
  dataset_size: 26681

Verifiable Code Reasoning

Execution-verified Python coding problems with step-by-step chain-of-thought.

Only solutions that pass sandboxed unit tests are kept. Failed executions are discarded.

Property	Detail
Verification	Multiprocess sandbox + timeout + multi-test
Dedup	Example id + normalized code hash
Balance	Category and difficulty quotas
Resume	Hub checkpoints (train.jsonl, progress.json)
License	MIT

Current size: 25 verified examples (target 1,000,000).

Schema

Field	Description
problem	Natural language problem
signature	Python function signature
reasoning	Numbered chain-of-thought
code	Reference solution
tests	JSON string of input/output test list
category	array, hashmap, dp, ...
difficulty	easy / medium / hard
sft_text	Ready-to-train text block
verified	Always true in this release
code_hash	Deduplication key

Note: tests is stored as a JSON string so Arrow/HF keeps a uniform schema across problems whose outputs are ints, bools, or lists.

Load

from datasets import load_dataset
import json
ds = load_dataset("smshahbaj/verifiable-code-reasoning")
row = ds["train"][0]
print(row["sft_text"][:400])
tests = json.loads(row["tests"])

SFT format (sft_text)

### Problem
...

### Reasoning
...

### Solution
```python
...


## Construction

1. 25 algorithmic generator families
2. At least 3 unit tests per example
3. Sandbox execution filter (fail = drop)
4. Quotas against template collapse
5. Checkpoint upload every 2000 new verifies

## Intended use

- SFT on code + reasoning
- Process / outcome reward modeling
- Filtering noisy synthetic traces

## Limitations

- Coverage equals the generator bank (not full competitive programming)
- Python only
- Always validate downstream models on real held-out benchmarks

## Citation

```bibtex
@misc{verifiable_code_reasoning,
  title  = {Verifiable Code Reasoning},
  author = {smshahbaj},
  year   = {2026},
  url    = {https://huggingface.co/datasets/smshahbaj/verifiable-code-reasoning}
}