README.md

metadata

pretty_name: Code Reasoning Data
language:
  - en
task_categories:
  - text-generation
tags:
  - code
  - sft
  - reinforcement-learning
license: other
license_name: source-specific-licenses
license_link: LICENSE
size_categories:
  - 10K<n<100K
configs:
  - config_name: kodcode_sft
    default: true
    data_files:
      - split: train
        path: data/kodcode/sft/train.parquet
      - split: validation
        path: data/kodcode/sft/validation.parquet
  - config_name: kodcode_rl
    data_files:
      - split: train
        path: data/kodcode/rl/train.parquet
      - split: validation
        path: data/kodcode/rl/validation.parquet
  - config_name: taco_leetcode_sft
    data_files:
      - split: train
        path: data/taco_leetcode/sft/train.parquet
      - split: validation
        path: data/taco_leetcode/sft/validation.parquet
  - config_name: taco_leetcode_rl
    data_files:
      - split: train
        path: data/taco_leetcode/rl/train.parquet
      - split: validation
        path: data/taco_leetcode/rl/validation.parquet

Code Reasoning Data

Code-training data with SFT answers, executable RL tests, and category labels.

Configuration	Train	Validation
kodcode_sft	35,974	100
kodcode_rl	35,974	100
taco_leetcode_sft	21,779	100
taco_leetcode_rl	17,249	100

from datasets import load_dataset

ds = load_dataset("./dataset", "kodcode_sft")
# After upload, replace ./dataset with your Hugging Face dataset repository ID.

SFT: use messages or prompt / response. RL: use prompt and reward_model.ground_truth. Apply the target model's template at training time. Use only the documented input/target fields; labels and tests are metadata.

KodCode uses ten primary categories; TACO/LeetCode uses seven category-removal groups. Their taxonomies and historical ablation memberships are separate.

The TACO SFT validation split is new in this release. Other splits are historical. These configurations are separate experiments, not a joint SFT-to-RL split. Historical TACO RL validation overlaps the original SFT pool on 71 problems. See TACO/LeetCode notes for historical export.

pip install -r requirements.txt
python scripts/validate_release.py --hf

Source-specific licenses apply; see LICENSE and NOTICE. Reward scripts require Linux; usage and limits are in verification notes.