随着AI大模型迭代升级，如今量化私募基金对AI大模型底层技 术的研发布局，日益侧重算法优化。在这个过程，产学研的结 合将是他们在大模型底层技术研发方面取得突破的"捷径"。 作者：陈植 封图：图虫创意 5月以来，全球大模型研发公司在大模型语义理解、多模态等方面的"较劲"悄然升级。 中国深度求索（DeepSeek）公司表示，DeepSeek R1模型已完成小版本升级，令大模型的思维深 度与推理能力显著提升。 国内量化私募基金念空科技与上海交通大学计算机学院开展合作，提出一种全新的大模型训练框架 （SASR），并发表论文投向全球顶级人工智能会议期刊NIPS。 念空科技创始人王啸在6月3日接受本报记者专访时表示，这项全新的大模型训练框架（SASR）， 在GSM8K任务中，在仅使用1.5B模型的情况下，准确率就超过了80%，接近GPT-4o的表现；而在 KK逻辑推理任务中，其准确率比GPT-4o还高出约9个百分点。SASR让通用大模型变得更"聪明"。 他告诉记者，当前大模型技术的训练框架，主要围绕监督微调(SFT)和强化学习(RL)，所谓监督微 调(SFT)就是不断给大模型输入资料和案例进行监督训练，相当于"刷题"； ...

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]