Core Viewpoint - The article discusses the challenges and limitations of using alternative data for training large models in the context of artificial intelligence, particularly in robotics, emphasizing that while alternative data can reduce costs, it often compromises the model's generalization capabilities [6][30][40]. Group 1: Challenges in Training Large Models - Training large models, especially in robotics, requires vast amounts of real-world interaction data, which is costly to obtain [2][4]. - Researchers are exploring alternative data sources to balance cost and training effectiveness, but achieving this balance is complex [5][8]. Group 2: Alternative Data Strategies - Various methods for obtaining alternative data include simulation, human videos, and handheld gripper devices, each with its own strengths and weaknesses [10][12][13]. - While these methods have produced significant research outcomes, they represent compromises that may weaken the inherent capabilities of large-scale learning models [14]. Group 3: Limitations of Alternative Data - The reliance on alternative data can lead to a disconnect between the training environment and real-world applications, limiting the model's ability to generalize effectively [26][28]. - The design decisions made when creating alternative data can significantly impact the overlap between successful strategies in real-world scenarios and those learned from alternative data [23][24]. Group 4: Importance of Real-World Data - Real-world data is essential for developing models with broad generalization capabilities, as it allows models to learn the true mechanisms of the world [36]. - Alternative data should be viewed as a supplementary source of knowledge rather than a replacement for real-world experience [37][38]. Group 5: The Concept of "Sporks" - The term "sporks" is used to describe alternative data approaches that attempt to combine the benefits of large-scale training with the cost-effectiveness of alternative data [39][40]. - Other "spork" methods include hybrid systems that combine manual design with learning components, aiming to mitigate the high data demands of machine learning [41][42].