paper_metadata.json

{
  "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",
    "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-think-SFT",
    "base_model": "Qwen3-14B-Base",
    "training_method": "Distill from Qwen3-32B-Instruct (thinking mode) through Reject Sampling",
    "task_focus": "math-reasoning",
    "upload_date": "2025-07-05T00:53:17.090139"
  },
  "repository": {
    "repo_name": "ReasoningTransferability/UniReason-Qwen3-14B-think-SFT",
    "huggingface_url": "https://huggingface.co/ReasoningTransferability/UniReason-Qwen3-14B-think-SFT"
  }
}