22 lines
1007 B
JSON
22 lines
1007 B
JSON
{
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"paper": {
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"title": "Does Math Reasoning Improve General LLM Capabilities? Understanding Transferability of LLM Reasoning",
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"arxiv_id": "2507.00432",
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"arxiv_url": "https://arxiv.org/abs/2507.00432",
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"authors": [
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"Research Team"
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],
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"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?"
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},
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"model": {
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"name": "UniReason-Qwen3-14B-RL",
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"base_model": "qwen3-14b",
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"training_method": "RL-GRPO",
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"task_focus": "math-reasoning",
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"upload_date": "2025-07-03T18:49:36.282079"
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},
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"repository": {
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"repo_name": "ReasoningTransferability/UniReason-Qwen3-14B-RL",
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"huggingface_url": "https://huggingface.co/ReasoningTransferability/UniReason-Qwen3-14B-RL"
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}
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} |