华为盘古718B模型最新成绩：开源第二

Core Viewpoint - Huawei has emerged as a strong competitor in the AI model landscape, particularly highlighted by its performance in the latest SuperCLUE benchmark evaluation, showcasing its capabilities in various dimensions of AI model performance [1][2]. Group 1: Model Rankings and Performance - The top three models in the SuperCLUE evaluation based on open-source and domestic criteria are: 1. DeepSeek-V3.1-Terminus-Thinking 2. openPangu-Ultra-MoE-718B 3. Qwen3-235B-A22B-Thinking-2507 [5]. - Huawei's openPangu-Ultra-MoE-718B model, with 718 billion parameters, stands out due to its unique training philosophy that emphasizes quality over sheer data volume [6][35]. Group 2: Data Quality and Training Strategy - The openPangu team adheres to three core principles in post-training data construction: quality first, diversity coverage, and complexity adaptation [10][21]. - A comprehensive framework for data generation, scientific selection, and precise enhancement has been established to ensure high data quality, which is crucial for improving the model's reasoning capabilities in complex scenarios [13][35]. Group 3: Pre-training and Optimization Techniques - The pre-training process for openPangu-718B is divided into three stages: General, Reasoning, and Annealing, each focusing on different aspects of knowledge and reasoning enhancement [15][35]. - The model employs a "Critique Internalization" mechanism to mitigate hallucinations, allowing it to self-evaluate its reasoning process and improve output reliability [19][22]. Group 4: Tool Usage and Agent Capabilities - To enhance the model's ability to use tools, the team developed the ToolACE framework, which generates high-quality, complex multi-tool interaction data for training [23][26]. - The model's training includes a three-step post-training fine-tuning scheme to optimize performance, ensuring a balance between underfitting and overfitting [27][29]. Group 5: Technical Innovations and Industry Implications - The systematic technical innovations across various training stages contribute to the superior performance of openPangu-718B, setting a valuable example for the industry on the importance of meticulous technical refinement and deep insights into core challenges [35].