Core Viewpoint - The article discusses the evolving landscape of large models in autonomous driving, highlighting the focus on lightweight solutions, hardware adaptation, knowledge distillation, and advanced reasoning paradigms like CoT and VLA+ reinforcement learning as key areas for future development [1][2]. Group 1: Course Introduction - The course aims to explore cutting-edge optimization methods for large models, focusing on parameter-efficient computation, dynamic knowledge expansion, and complex reasoning [2]. - It addresses the core challenges in model optimization, including pruning, quantization, retrieval-augmented generation (RAG), and advanced reasoning paradigms [3]. Group 2: Problems Addressed by the Course - The course provides a systematic understanding of large model knowledge, helping students build a coherent theoretical framework [3]. - It assists students in combining theoretical knowledge with practical coding skills, enabling them to replicate research papers and develop new models [3]. - The course offers guidance on writing and submitting academic papers, addressing common challenges faced by students [3]. Group 3: Enrollment Information - The course limits enrollment to 6-8 students per session [4]. - It targets individuals with a background in deep learning or machine learning, familiarity with Python, and a passion for research [6]. Group 4: Course Outcomes - Participants will gain insights into classic and cutting-edge papers in the field, enhancing their understanding of key algorithms and principles [9]. - The course includes a structured approach to writing and revising academic papers, culminating in the production of a draft [9]. Group 5: Course Structure - The course spans 12 weeks of online group research followed by 2 weeks of paper guidance and a 10-week maintenance period [9]. - It covers various topics, including model pruning, quantization, and advanced reasoning techniques, with a focus on practical applications [19].

Core Viewpoint - The article discusses the advancements in large models (LLMs) for autonomous driving, highlighting the need for optimization in efficiency, knowledge expansion, and reasoning capabilities as the technology matures [2][3]. Group 1: Development of Large Models - Companies like Li Auto and Huawei are implementing their own VLA and VLM solutions, indicating a trend towards the practical application of large models in autonomous driving [2]. - The focus for the next generation of large models includes lightweight design, hardware adaptation, knowledge distillation, quantization acceleration, and efficient fine-tuning [2][3]. Group 2: Course Introduction - A course is being offered to explore cutting-edge optimization methods for large models, focusing on parameter-efficient computation, dynamic knowledge expansion, and complex reasoning [3]. - The course aims to address core challenges in model optimization, including pruning, quantization, retrieval-augmented generation (RAG), and advanced reasoning paradigms like Chain-of-Thought (CoT) and reinforcement learning [3][4]. Group 3: Enrollment and Requirements - The course will accept a maximum of 8 students per session, targeting individuals with a background in deep learning or machine learning who are familiar with Python and PyTorch [5][10]. - Participants will gain a systematic understanding of large model optimization, practical coding skills, and insights into academic writing and publication processes [8][10]. Group 4: Course Outcomes - Students will learn to combine theoretical knowledge with practical coding, develop their own research ideas, and produce a draft of a research paper [8][9]. - The course includes a structured timeline with specific topics each week, covering model pruning, quantization, efficient fine-tuning, and advanced reasoning techniques [20].

Core Insights - The article discusses the evolving landscape of large models in autonomous driving, highlighting the focus on lightweight solutions, hardware compatibility, knowledge distillation, and efficient fine-tuning of large models [1] - It emphasizes the importance of advanced reasoning paradigms such as Chain-of-Thought (CoT) and VLA combined with reinforcement learning in enhancing spatial perception capabilities [1] Group 1: Course Overview - The course aims to explore cutting-edge optimization methods for large models, focusing on parameter-efficient computation, dynamic knowledge expansion, and complex reasoning [2] - Key challenges in model optimization include parameter compression through pruning and quantization, dynamic knowledge injection techniques, and advanced reasoning paradigms [2][3] Group 2: Enrollment and Requirements - The course is limited to 6-8 participants per session, targeting individuals with a foundational understanding of deep learning and machine learning [4][8] - Participants are expected to have basic programming skills in Python and familiarity with PyTorch, along with a genuine interest in research [8] Group 3: Course Outcomes - The course aims to provide a systematic understanding of large model optimization, helping participants develop their own research ideas and enhance their coding skills [6][7] - Participants will receive guidance on writing and submitting academic papers, including methodologies for drafting and revising manuscripts [6][7] Group 4: Course Structure - The course spans 12 weeks of online group research followed by 2 weeks of paper guidance, covering topics such as model pruning, quantization, and dynamic knowledge expansion [7][18] - Each week focuses on specific themes, including advanced reasoning techniques and collaborative multi-agent systems [18][20] Group 5: Additional Information - The course will utilize publicly available datasets and baseline codes tailored to specific applications, ensuring practical relevance [15][16] - Participants will engage in discussions and hands-on experiments using mainstream large models like LLaMA and GPT [2][18]

Core Insights - The article emphasizes the rapid development of large language models (LLMs) and multimodal models, focusing on enhancing model efficiency, expanding knowledge capabilities, and improving reasoning performance as key research areas in artificial intelligence [1][2]. Course Objectives - The course aims to systematically explore cutting-edge optimization methods for large models, addressing challenges in parameter-efficient computation, dynamic knowledge expansion, and complex reasoning [1][2]. Enrollment Details - The course will accept 6 to 8 participants per session [3]. Target Audience - The course is designed for master's and doctoral students in the field of large models, individuals seeking to enhance their resumes for graduate studies abroad, and professionals in artificial intelligence looking to deepen their understanding of algorithm theory and research skills [4]. Course Outcomes - Participants will gain insights into classic and cutting-edge papers, coding implementations, and methods for writing and submitting research papers, thereby developing a clearer understanding of the subject matter [3][4]. Enrollment Requirements - Basic requirements include familiarity with deep learning/machine learning, basic knowledge of large model algorithms, proficiency in Python, and experience with PyTorch [5]. Course Structure - The course spans 12 weeks of online group research, followed by 2 weeks of paper guidance, and includes a maintenance period of 10 weeks for paper development [10]. Learning Requirements - Participants are expected to engage actively in discussions, complete assignments on time, and maintain academic integrity throughout the course [12]. Course Outline - The curriculum covers various topics, including model pruning, quantization, dynamic knowledge expansion, and advanced reasoning paradigms, with a focus on practical applications and coding [16][18].

Core Insights - The article discusses the rapid development of large language models (LLMs) and multimodal models, focusing on enhancing model efficiency, expanding knowledge capabilities, and improving reasoning performance as core issues in current AI research [1][2]. Course Overview - The course systematically explores cutting-edge optimization methods for large models, emphasizing three key areas: parameter-efficient computation, dynamic knowledge expansion, and complex reasoning [1]. - It addresses core challenges in model optimization, including lightweight methods such as pruning, sparsification, and quantization for parameter compression; dynamic knowledge injection techniques like retrieval-augmented generation (RAG) and parameter-efficient fine-tuning (PEFT) for knowledge expansion; and advanced reasoning paradigms such as chain-of-thought (CoT) and reinforcement learning optimization (GRPO) for reasoning enhancement [1]. Course Objectives - The course aims to help students systematically master key theoretical knowledge in specified directions and develop a clearer understanding of the content [5]. - It seeks to bridge the gap for students who lack direction and practical skills, enabling them to combine theoretical knowledge with coding practice and lay the groundwork for developing new models [5]. - The course also focuses on improving students' academic writing skills, providing guidance on manuscript preparation and submission [5]. Target Audience - The course is designed for master's and doctoral students in the field of large models, those seeking to enhance their resumes for graduate studies abroad, and professionals in the AI field looking to systematically improve their algorithmic theory and writing skills [6]. Admission Requirements - Basic requirements include a foundational understanding of deep learning/machine learning, familiarity with Python syntax, and experience with PyTorch [7]. Course Structure - The course consists of 12 weeks of online group research followed by 2 weeks of paper guidance, culminating in a 10-week paper maintenance period [11]. - Students will analyze classic and cutting-edge papers, understand key algorithms and principles, and develop their research ideas [11]. Weekly Breakdown - The course covers various topics, including model pruning, quantization, dynamic knowledge expansion, advanced reasoning techniques, and multimodal understanding [16][18]. - Each week includes specific themes and outputs, such as determining research ideas, optimizing model size and performance, and enhancing coding capabilities [16][18]. Additional Resources - The course provides access to datasets from public sources and baseline code tailored to specific applications [13][14]. - Essential papers and resources are recommended for foundational knowledge and advanced techniques in model optimization [15][17].

梦晨 发自 凹非寺 量子位 | 公众号 QbitAI 与此同时，Nexusflow的CTO Jian Zhang （同样是清华校友）也成为英伟达应用研究总监。 但Nexusflow这家公司到底何去何从还没有正式消息。 贾扬清LeptonAI之后，又一家华人AI创业公司卖身英伟达。 黄仁勋亲自出马招募了Nexusflow的几位联合创始人： △ 左：焦剑涛，右：朱邦华 后续Nexusflow的早期投资者证实，公司已被英伟达收购。 Nexusflow如何搭上英伟达这条线或许有迹可循。 另一位联创UC伯克利教授 Kurt Keutzer 在2025年参与了英伟达与学术界合作的项目 Efficient AI ，致力于开发和优化GPU加速的高效AI 计算。 焦剑涛 ，前Nexusflow CEO，UC伯克利副教授，斯坦福博士毕业，清华2011年本科特奖得主。加入英伟达任研究总监及杰出科学家 （Distinguished Scientist） 朱邦华 ，前Nexusflow联创，将加入华盛顿大学任助理教授，UC伯克利毕业，本科也来自清华。加入英伟达任Principal Research Scientist Kurt Keu ...