Core Insights - The competition among global large model development companies has intensified, particularly in semantic understanding and multimodal capabilities since May [2] - Domestic quantitative private equity funds are also entering the race, achieving breakthroughs in AI large model foundational technology [2][5] - A new training framework (SASR) proposed by NianKong Technology in collaboration with Shanghai Jiao Tong University has shown promising results, achieving over 80% accuracy on the GSM8K task with a 1.5B model, nearing GPT-4o's performance [2][4] Group 1: Training Framework and Algorithm Optimization - The current training frameworks for large models primarily focus on Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL), with the challenge being to optimize the balance between these two methods [3][8] - The new training framework aims to dynamically adjust the relationship between SFT and RL, allowing the model to become "smarter" without increasing data volume [3][9] - The innovative training framework has been applied in quantitative investment strategy development, achieving approximately 80% market prediction accuracy compared to traditional models [4][13] Group 2: Industry Trends and Collaborations - Many quantitative private equity firms are establishing AI Labs to focus on foundational technology research for large models, emphasizing algorithm optimization [6][11] - The integration of academic research and private equity expertise is seen as a shortcut to breakthroughs in large model foundational technology [5][11] - The emergence of smarter large models with lower parameter counts but superior overall capabilities is attributed to innovations in training frameworks and algorithm optimization [10] Group 3: Future Directions and Challenges - The ability of large models to become "smarter" in various vertical fields depends on high-quality data and effective training modes [12] - NianKong Technology aims to empower large models to excel in more vertical fields, enhancing China's competitiveness in the global AI landscape [14]