Expanding Reasoning Potential in Foundation Model by Learning Diverse Chains of Thought Patterns
Xuemiao Zhang · Can Ren · Chengying Tu · Rongxiang Weng · Shuo Wang · Hongfei Yan · Jingang Wang · Xunliang Cai
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Xuemiao Zhang · Can Ren · Chengying Tu · Rongxiang Weng · Shuo Wang · Hongfei Yan · Jingang Wang · Xunliang Cai
Xuemiao Zhang · Can Ren · Chengying Tu · Rongxiang Weng · Hongfei Yan · Jingang Wang · Xunliang Cai
The advancement of large language models (LLMs) struggles with the scarcity of high-quality, diverse training data. To address this limitation, we propose LinkSyn, a KP-graph-based synthesis framework that for the first time enables flexible control over discipline and difficulty distributions wh...
Xuemiao Zhang · Chengying Tu · Can Ren · Rongxiang Weng · Hongfei Yan · Jingang Wang · Xunliang Cai
Mid-training has become critical for enhancing the knowledge and reasoning ability of large language models (LLMs), especially through the utilization of large-scale synthetic data. However, existing data synthesis methods often generate simplistic and homogeneous QA pairs, with limited scale and...