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A Survey on LLM Mid-Training
Chengying Tu, Xuemiao Zhang, Rongxiang Weng +6
Recent advances in foundation models have highlighted the significant benefits of multi-stage training, with a particular emphasis on the emergence of mid-training as a vital stage…
LongCat-Flash Technical Report
Meituan LongCat Team, Bayan, Bei Li +179
We introduce LongCat-Flash, a 560-billion-parameter Mixture-of-Experts (MoE) language model designed for both computational efficiency and advanced agentic capabilities. Stemming f…
LinkQA: Synthesizing Diverse QA from Multiple Seeds Strongly Linked by Knowledge Points
Xuemiao Zhang, Can Ren, Chengying Tu +4
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 novel knowledg…
Large-Scale Diverse Synthesis for Mid-Training
Xuemiao Zhang, Chengying Tu, Can Ren +4
The scarcity of high-quality, knowledge-intensive training data hinders the development of large language models (LLMs), as traditional corpora provide limited information. Previou…
Enhancing LLMs via High-Knowledge Data Selection
Feiyu Duan, Xuemiao Zhang, Sirui Wang +4
The performance of Large Language Models (LLMs) is intrinsically linked to the quality of its training data. Although several studies have proposed methods for high-quality data se…
Preference Curriculum: LLMs Should Always Be Pretrained on Their Preferred Data
Xuemiao Zhang, Liangyu Xu, Feiyu Duan +5
Large language models (LLMs) generally utilize a consistent data distribution throughout the pretraining process. However, as the model's capability improves, it is intuitive that…
FRAME: Boosting LLMs with A Four-Quadrant Multi-Stage Pretraining Strategy
Xuemiao Zhang, Feiyu Duan, Liangyu Xu +5
Large language models (LLMs) have significantly advanced human language understanding and generation, with pretraining data quality and organization being crucial to their performa…
FIRE: Flexible Integration of Data Quality Ratings for Effective Pre-Training
Liangyu Xu, Xuemiao Zhang, Feiyu Duan +4
Selecting high-quality data can improve the pretraining efficiency of large language models (LLMs). Existing methods generally rely on heuristic techniques or single quality signal…