{
  "paper": {
    "title": "Does Math Reasoning Improve General LLM Capabilities? Understanding Transferability of LLM Reasoning",
    "arxiv_id": "2507.00432",
    "arxiv_url": "https://arxiv.org/abs/2507.00432",
    "authors": [
      "Research Team"
    ],
    "abstract": "Math reasoning has become the poster child of progress in large language models (LLMs), with new models rapidly surpassing human-level performance on benchmarks like MATH and AIME. But as math leaderboards improve week by week, it is worth asking: do these gains reflect broader problem-solving ability or just narrow overfitting?"
  },
  "model": {
    "name": "UniReason-Qwen3-14B-RL",
    "base_model": "qwen3-14b",
    "training_method": "RL-GRPO",
    "task_focus": "math-reasoning",
    "upload_date": "2025-07-03T18:49:36.282079"
  },
  "repository": {
    "repo_name": "ReasoningTransferability/UniReason-Qwen3-14B-RL",
    "huggingface_url": "https://huggingface.co/ReasoningTransferability/UniReason-Qwen3-14B-RL"
  }
}