后训练的「分」与「合」，SFT&RL 大一统才是正解？

Group 1 - The article discusses the limitations of the traditional "SFT followed by RL" paradigm in post-training for AI models, suggesting a unified approach that combines both methods [7][9][10] - It highlights the importance of post-training in aligning the model's capabilities with human values and preferences, addressing the challenges of "catastrophic forgetting" and overfitting associated with SFT [8][11][12] - The emerging trend in the industry is to explore a unified framework for post-training that leverages the strengths of both SFT and RL, rather than treating them as separate processes [10][15][17] Group 2 - The article evaluates the competitive landscape of AI hardware among major players like Meta, OpenAI, Apple, and Google, questioning whether AI hardware will become a new essential or merely a passing trend [2] - It raises questions about the user experience with AI hardware, such as whether it will truly replace traditional devices or simply serve as an additional feature [2][3] - The potential for innovative AI hardware forms to integrate seamlessly into daily life is explored, along with the implications for user interaction and technology adoption [2][3] Group 3 - The article examines the role of generative AI in search, debating whether it will serve as a replacement for traditional search engines or act as a growth engine for expanding user queries and intentions [3] - It discusses how multimodal interactions and conversational AI are redefining task completion for users, potentially enhancing the value of advertising and commercial opportunities [3] - Google's strategy of gradually integrating AI capabilities into its products, rather than waiting for full technological maturity, reflects a proactive approach to product development and market positioning [3]