一直霸榜的pi0.5，被中国的模型干下来了！！！

Core Viewpoint - The article highlights the breakthrough of the "Spirit v1.5" model developed by Qianxun Intelligent Team, which has surpassed the international benchmark model pi0.5, marking a significant advancement for China in the field of embodied intelligence models [2]. Performance Comparison - The ranking of models in the RoboChallenge shows Spirit v1.5 leading with a score of 66.09 and a success rate of 50.33%, followed by pi0.5 with a score of 61.84 and a success rate of 42.67% [4]. Data Collection Challenges - The article discusses the limitations of relying on "clean" data for training models, which can lead to low diversity and scalability issues. Clean data often lacks the complexity of real-world scenarios, hindering the model's ability to generalize [5][7]. Training Methodology - Spirit v1.5 employs a training methodology that does not depend on highly curated "clean" demonstration data. Instead, it utilizes a diverse data collection paradigm that allows for the natural integration of multiple sub-tasks and atomic skills, enhancing the model's adaptability to real-world complexities [8][14]. Transfer Efficiency - Experimental results indicate that models pre-trained on diverse data exhibit significantly higher transfer efficiency on new tasks compared to those trained on traditional demonstration data, requiring less computational resources to achieve similar performance [9][12]. Scaling Findings - The article notes that as the scale of diverse experiences increases, the model's transfer efficiency improves, leading to a continuous decrease in validation error for new tasks. This suggests that task diversity is more critical than the number of single-task demonstrations [13][16]. Paradigm Shift in Pre-training - Spirit v1.5 represents a fundamental shift in the field of robotic learning, moving away from the reliance on highly curated datasets. The findings suggest that unstructured diversity serves as a better teacher for robust pre-training, enabling models to develop a foundational "physical intuition" for better adaptability in real-world environments [14].