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 diversity. To address this, we propose BoostQA, a novel framework designed to synthesize large-scale, diverse, and high-quality QA data for mid-training. BoostQA introduces model probes during mid-training for the first time and implements STEM-focused multi-grade synthesis to boost data diversity as well as high-difficulty synthesis to alleviate difficulty degradation, followed by answer refinement to further improve quality. Extensive experiments by mid-training Llama-3 8B demonstrate that using only 20B-token BoostQA data achieves a significant average improvement of **12.74%** on MMLU and CMMLU over the pre-training baseline. After mid-training on 500B tokens, including 100B-token BoostQA data, our model achieves SOTA average results across benchmarks among mainstream models of comparable size. BoostQA also demonstrates robust scalability, with performance consistently improving as model size, data volume, and initial FLOPs scale.

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 while balancing KP coverage and popularity. LinkSyn extracts KPs from question-answering (QA) seed data and constructs a KP graph to synthesize diverse QA data from multiple seeds strongly linked by KPs and sampled from graph walks. Specifically, LinkSyn incorporates (1) a knowledge value function to guide the adjustment of path sampling probability and balance KP coverage and popularity during graph walks; (2) diffusion-based synthesis via a strong reasoning model by leveraging multiple seeds with dense logical associations along each path; and (3) high-difficulty QA enhancement within given disciplines by flexible difficulty adjustments. By executing LinkSyn, we synthesize LinkQA, a diverse multi-disciplinary QA dataset with 50B tokens. Extensive experiments on Llama-3 8B demonstrate that continual pre-training with LinkQA yields an average improvement of 11.51% on MMLU and CMMLU, establishing new SOTA results. LinkQA consistently enhances performance across model size and initial FLOPs scales.