README.md

metadata

license: mit
language:
  - en
tags:
  - code
pretty_name: ' ArcherCodeR'
size_categories:
  - 1K<n<10K
task_categories:
  - text-generation
viewer: false

✨ ArcherCodeR

🏹️ Reinforcement Learning for Enhanced Code Reasoning in LLMs 🎯

Overview

ArcherCodeR-Dataset is a dataset of verifiable, challenging, and diverse coding questions (6.7K). This dataset is used to train the ArcherCodeR model series, which consists of code reasoning models trained using large-scale rule-based reinforcement learning with carefully designed datasets and training recipes.

We select, clean, and curate coding problems from open-source datasets, including

• agentica-org/DeepScaleR-Preview-Dataset
• deepmind/code_contests
• open-r1/codeforces

🔍 Key Notes:

• Both code_contests (DeepMind) and codeforces (Open-r1) datasets include regenerated test cases to mitigate false positives.
• Significant prompt duplication exists across sources. When duplicates occur, code_contests or codeforces data takes priority.

For more details on data processing, please refer to our Zhihu article.

Technical Report

The technical report will be released soon.